MCVEAN (2002) showed that predictions for r 2,a
|
|
- Arabella Harrington
- 6 years ago
- Views:
Transcription
1 oyright Ó 2008 by the Genetics Society of America DOI: /genetics Note The Influence of Gene onversion on Linkage Diseuilibrium Around a Selective Swee Danielle A. Jones 1 and John Wakeley Deartment of Organismic and Evolutionary Biology, Harvard University, ambridge, Massachusetts Manuscrit received June 4, 2008 Acceted for ublication July 17, 2008 ABSTRAT In a 2007 article, McVean studied the effect of recombination on linkage diseuilibrium (LD) between two neutral loci located near a third locus that has undergone a selective swee. The results demonstrated that two loci on the same side of a selected locus might show substantial LD, whereas the exected LD for two loci on oosite sides of a selected locus is zero. In this article, we extend McVean s model to include gene conversion. We show that one of the conclusions is strongly affected by gene conversion: when gene conversion is resent, there may be substantial LD between two loci on oosite sides of a selective swee. MVEAN (2002) showed that redictions for r 2,a commonly used measure of LD introduced by Hill and Robertson (1968), deend on the correlations in coalescence times for a air of loci, which in turn deend on the recombination rate between the loci. In alying this result to LD near a locus that has undergone a selective swee, McVean (2007) develoed a new model that features two neutral loci artially linked to the selected locus. He assumed that recombination could occur between each air of loci and that the swee had a articularly simle structure: a star tree. Figure 1 deicts the model, in which a samle at the two neutral loci is taken at the resent time 0, just after the swee has finished. The swee is assumed to have occurred uickly and to have begun at time t M in the ast, measured in units of 2N e generations, where N e is the (diloid) coalescent effective oulation size (Sjödin et al. 2005). On the basis of the work of Maynard Smith and Haigh (1974) and others (Kalan et al. 1989; Stehan et al. 1992; Durrett and Schweinsberg 2004), McVean (2007) used t M ¼ 0.1, and we adot this value below. McVean (2007) tested the validity of this aroximate model by comaring its redictions to the results of fully stochastic simulations of a swee and found them to be largely accurate. McVean (2007) allowed for recombination (recirocal exchange of genetic material as in a single crossover event) but not for gene conversion (nonrecirocal 1 orresonding author: Biolabs 4092, 16 Divinity Ave., ambridge, MA djones@fas.harvard.edu exchange of short tracks of genetic material). However, there is a growing body of evidence for the imortance of gene conversion in shaing genetic variation in humans (Frisse et al. 2001; Jeffreys and May 2004; Padhukasahasram et al. 2004; hen et al. 2007; Gay et al. 2007) and models that do not feature gene conversion, therefore, do not comletely cature the biological causes, and exectations, of genetic variability. Our aim here is to incororate gene conversion into the model and to ask whether this changes the results. We focus on the case in which variation at the two neutral loci is due to mutations that occurred during the neutral hase shown in Figure 1B. In this case, the two neutral loci can be olymorhic only if they do not coalesce along with the selected allele at time t M in Figure 1B. Without recombination or gene conversion, resent-day samles at the two neutral loci will always remain linked to the selected allele and will certainly coalesce with the selected allele. Recombination and gene conversion allow the loci to escae the swee with some robability and to coalesce during the neutral hase, where they might also exerience mutations. To make a rediction for r 2 secifically s 2 d of Ohta and Kimura (1971) it is necessary to comute the exectation of the roduct of the coalescence times at two loci for each of the three samle configurations (A, B, and ) in Figure 1A (McVean 2002). Briefly, in the three-locus model of McVean (2007), for each of these three samling configurations, we must comute the robability that the two neutral loci are in configuration A, B, or at the start of the neutral hase looking back (i.e., time t M ), at which oint all chromosomes in the Genetics 180: (October 2008)
2 1252 D. A. Jones and J. Wakeley reresent configurations. The exected roduct of coalescence times at the two neutral loci, samled at resent when all chromosomes ossess the selected allele (denoted by the subscrit S), are the averages over the three ancestral configurations. The exected coalescence time for two chromosomes, i and j, samled at locus X is written as t x ij and for chromosomes k and l, at locus Y, it is written as t y kl. For configuration A, we have E S ½t x ij ty ijš ¼f AA E W ½t x ij ty ijš 1 f AB E W ½t x ij ty ikš 1 f A E W ½t x ij ty klš; Figure 1. This is an adatation of Figure 1 of McVean (2007). (A) The three configurations are A, two neutral loci are samled from two chromosomes; B, two neutral loci are samled from three chromosomes; and, two neutral loci are samled from four chromosomes. (B) The selection event occurs as a raid selective swee during which only crossingover events can occur. During the neutral hase, coalescent events can also occur. oulation have the ancestral tye, or wild tye, at the selected locus. There are nine such robabilities in total, one for each air of configurations. These nine robabilities are denoted using f, with subscrits to which is Euation 9 in McVean (2007). The robabilities f AA, f AB, and so on, deend on t M and on the rates of recombination and gene conversion. The exected values on the right-hand side above are those exected during the neutral hase (W stands for wild-tye allele) and are given in Euation 10 of McVean (2007). The redictions about LD deend strongly on the relative osition of the selected locus comared to the neutral loci. McVean considered two cases: (1) the selected locus is located halfway between the two neutral loci (NSN) and (2) the selected locus is located on one side of the two neutral loci (SNN). All of the derivations described above are done searately for these two cases. onsidering only recombination, McVean redicted substantial LD for SNN, because in this case both neutral loci can escae the swee yet remain linked to each other at the beginning of the neutral hase. For the NSN case, the model without gene conversion redicts no LD between the two neutral loci. As McVean Figure 2. There are four cases: SNN without gene conversion and with gene conversion and NSN without gene conversion and with gene conversion. In all cases, during the selective swee hase, there are two arameters that describe the robability of escae via recombination: x ¼ 1 e ðrx=2þtm and y ¼ 1 e ðry=2þtm. During the neutral hase, the robability of recombination is R x ¼ 4N e r and R y ¼ 4N e r, where r is the er generation robability of recombination. The recombination distance between the two neutral loci is ket constant regardless of whether they are in the SNN or the NSN case. Thus for NSN, R x ¼ R y ¼ 1 2 4N er. When gene conversion is added to the model for both SNN and NSN, the gene conversion is restricted to the individual loci. The overall rate of gene conversion is k X ¼ k S ¼ k Y ¼ 4N e g for each locus, where g is the er generation robability of gene conversion.
3 Note 1253 Figure 3. This is an adatation of Figure 2 of McVean (2007) but, imortantly, it includes five novel transitions from configuration A to configurations A and B at the start of the neutral hase that are not ossible when only recombination is resent. The transitions reresented corresond to the transition robabilities resent in Table A1 and are in the same order excluding the five cases where the transition robability is zero as in Table A1. Also shown in brackets is the notation used for each configuration (McVean 2007). The notation for configuration A is [i S j S, i S j S ] which indicates that two chromosomes, i and j, were samled at locus X and locus Y, and both chromosomes ossess the selected allele. The samling configurations at locus X and locus Y are searated by a comma. notes, this reflects the symmetric nature of the recombination rocess for the NSN case: the robabilities of each of the three configurations at the beginning of the neutral hase (A, B, or ) are the same for each of the three samle configurations, so that s 2 d ¼ 0 (see Euation 14 of McVean 2007). This symmetry breaks down when gene conversion is included in the model because gene conversion at the selected locus allows both neutral loci to escae the swee yet remain linked at the beginning of the neutral hase. Figure 2 gives a grahical reresentation of the model for SNN and NSN, with and without gene conversion. We assume that when gene conversion occurs, it coies a tract length of m nucleotides, which is greater than the size of each locus and less than the distance between the loci. Thus, gene conversion acts on single loci indeendently. During the neutral hase, on the coalescent timescale, recombination events occur with rates R x and R y, and gene conversion events occur at rates k s, k x, and k y. Following McVean (2007), during the selection hase with duration t M there are two robabilities of escae via recombination: x ¼ 1 e tmrx=2 and y ¼ 1 e tmry=2. To these we add three robabilities of escae by gene conversion: g s ¼ 1 e tmks=2, g x ¼ 1 e tmkx=2, g y ¼ 1 e tmky=2. Tables A1 A6 in the aendix give all the terms needed to comute the robabilities f AA, f AB, and so on, for both SNN and NSN. We follow McVean (2007) in comuting transitions between the resent and the start of the neutral hase, using the intermediate configuration at the end of the selection hase; our Tables A1 A3 corresond to each of the three columns of Aendix A in McVean (2007) and our Tables A4 A6 corresond to each of the three columns of Aendix B in McVean (2007). Again, the key difference between the models is that in the NSN case gene conversion allows resent-day configuration A to remain in configuration A at the start of the neutral hase, whereas this is not ossible by recombination alone. Figure 3 shows the configurations that can be reached from samling configuration A at the resent; it is analogous to Figure 2inMcVean (2007) and shows five of the six novel con-
4 1254 D. A. Jones and J. Wakeley Figure 4. Exected LD for the NSN case. The y-axis is the amount of LD as redicted by s 2 D. The x-axis is the distance, in base airs, between the two neutral loci starting from a distance of 2 3 tract length, m, between them and increasing to 10,000 b. In this examle, m ¼ 300 b. The distance between either neutral locus and the selected site must be at least a tract length, so that any given gene conversion event converts only one locus; the two neutral loci are searated by a minimum of two tract lengths, 600 b. For this distance range, R Neutral ¼ nr 1 2mk. (A) f ¼ 0, there is no gene conversion. (B) f ¼ 1, there is the same amount of gene conversion as there is recombination. () f ¼ 5, there is 5 times as much gene conversion as recombination. This is a reasonable ratio for human data. (D) f ¼ 15, there is 15 times as much gene conversion as recombination. figurations (the bottom five transitions encomassed in a box) that can be reached when gene conversion is included in the model. Following McVean (2007), to generate redictions alicable to molecular data, we assume that the rates of recombination, R x and R y, deend linearly on the distance between the loci. For examle, if locus X is n nucleotides away from locus S, then R x ¼ nr, where r is the rate of recombination between adjacent nucleotides on the coalescent timescale. In addition, we assume that each locus is a single-nucleotide site, so that in the neutral hase all three loci have the same rate of conversion: k s ¼ k x ¼ k y ¼ mk, where k is the rate of initiation of a gene conversion event between two adjacent nucleotides on a coalescent timescale. Finally, we include a arameter, f ¼ k/r, which is the ratio of gene conversion to recombination. Note that, with this arameterization, the er generation robabilities of recombination and gene conversion in Figure 2 are given by r ¼ nr=4n e and g ¼ mk=4n e. To redict the likely effect of gene conversion on LD in human oulations, we substituted lausible genetic arameters from humans: r /b (Frisse et al. 2001); the ratio of gene conversion to recombination, f, has been estimated to be between 1.5 and 14 (Frisse et al. 2001; Jeffreys and May 2004; Padhukasahasram et al. 2004; hen et al. 2007; Gay et al. 2007); and tyical gene conversion tract lengths range from 50 to 500 b Figure 5. Exected LD for the SNN case. The y-axis is the amount of LD as redicted by s 2 D. The x-axis is the distance, in base airs, between the two loci starting from a distance of at least two tract lengths between them, to allow for comarison to the NSN case, and increasing to 10,000 b. In this examle, m ¼ 300 b; therefore, the starting distance between the two neutral loci is 600 b. For this distance range, R Neutral ¼ nr 1 2mk. (A) f ¼ 0, there is no gene conversion. (B) f ¼ 1, there is the same amount of gene conversion as there is recombination. () f ¼ 5, there is 5 times as much gene conversion as recombination. (D) f ¼ 15, there is 15 times as much gene conversion as recombination.
5 Note 1255 (Jeffreys and May 2004). To simlify our analysis, we assume a fixed tract length of 300 b (Jeffreys and Neumann 2002). Reeating our analysis with a 50-b tract length led to slightly higher levels of LD (results not shown). As Figures 4 and 5 show, adding gene conversion affects LD in both the NSN and the SNN case. In the SNN case, the effect of gene conversion is similar to the effect of recombination, so that increasing f decreases LD (Figure 5, B D). Adding gene conversion in the SNN case creates more oortunities for the two neutral loci to escae the swee indeendently, so LD between them is reduced. In the NSN case, gene conversion increases LD (Figure 4, B D). In this case, gene conversion has a ualitatively different effect. Gene conversion events at the middle (S) locus allow the two neutral loci to escae the swee together. Present-day samles of this tye may then be samles of an ancestral halotye that would otherwise have been lost during the swee. Looking forward in time, in the NSN case gene conversion can reserve the reexisting correlated genealogical structure between the outer loci. By incororating gene conversion into the threelocus model of McVean (2007), we have shown that LD is exected between two loci on oosite sides of a selected locus that has undergone a swee. Although we have focused only on a single air of neutral loci, our results have imlications for genomic scans for selective swees using extended halotye homozygosity (Sabeti et al. 2002), integrated extended halotye homozygosity (Voight et al. 2006), and long-range halotyes (Sabeti et al. 2007). In articular, gene conversion at the selected site will cause some fraction of resent-day chromosomes to show the selected allele but while sitting on an ancestral halotye. Using t M ¼ 0.1 as in McVean (2007), and assuming a tract length of m ¼ 300 and a ratio of gene conversion to recombination of f ¼ 5, the robability of samling such a chromosome is 1 e Then, among 100 chromosomes that all ossess the selected allele, we would exect to see about four of these aberrant halotyes, and the chance that all 100 chromosomes would show the classic, recombination-only swee attern would be Thus, it is ossible that many selected loci have been missed in the recent genomic scans for selection. LITERATURE ITED hen, J.-M., D. N. oer, N.huzhanova,.Ferec and G. P. Patrinos, 2007 Gene conversion: mechanisms, evolution and human disease. Nature 8: Durrett, R., and J. Schweinsberg, 2004 Aroximating selective swees. Theor. Poul. Biol. 66: Frisse, L., R. R. Hudson, A. Bartoszewicz, J. D. Wall, J. Donfack et al., 2001 Gene conversion and different oulation histories may exlain the contrast between olymorhism and linkage diseuilibrium. Am. J. Hum. Genet. 69: Gay, J., S. Myers and G. McVean, 2007 Estimating meiotic gene conversion rates from oulation genetic data. Genetics 177: Hill, W. G., and A. Robertson, 1968 Linkage diseuilibrium in finite oulations. Theor. Al. Genet. 38: Jeffreys, A. J., and. May, 2004 Intense and highly localized gene conversion activity in human meiotic crossover hot sots. Nat. Genet. 36: Jeffreys, A. J., and R. Neumann, 2002 Recirocal crossover asymmetry and meiotic drive in human recombination hot sot. Nat. Genet. 31: Kalan, N. L., R. R. Hudson and. H. Langley, 1989 The hitchhiking effect revisited. Genetics 123: Maynard Smith, J., and J. Haigh, 1974 The hitchhiking effect of a favourable gene. Genet. Res. 23: McVean, G., 2002 A genealogical interretation of linkage diseuilibrium. Genetics 16: McVean, G., 2007 The structure of linkage diseuilibrium around a selective swee. Genetics 175: Ohta, T., and M. Kimura, 1971 Linkage diseuilibrium between two segregating nucleotide sites under steady flux of mutations in a finite oulation. Genetics 68: Padhukasahasram, B., P. Marjoram and M. Nordborg, 2004 Estimating the rate of gene conversion on human chromosome 21. Am. J. Hum. Genet. 75: Sabeti, P.., D. E. Reich, J. M. Higgins, H. Z. P. Levine, D. J. Richter et al., 2002 Detecting recent ositive selection in the human genome from halotye structure. Nature 419: Sabeti, P.., P. Varilly, B.Fry, J.Lohmueller, E.Hostetter et al., 2007 Genome-wide detection and characterization of ositive selection in human oulations. Nature 449: Sjödin, P., I. Kaj, S. Krone, M. Lascoux and M. Nordborg, 2005 On the meaning and existence of an effective oulation size. Genetics 169: Stehan, W., T. H. E. Wiehe and M. W. Lenz, 1992 The effect of strongly selected substitutions on neutral olymorhisms: analytical results based on diffusion theory. Theor. Poul. Biol. 41: Voight, B. F., S. Kudaravalli, X. Wen and J. K. Pritchard, 2006 A ma of recent ositive selection in the human genome. PLoS Biol. 4(3): e72. ommunicating editor: N. Takahata APPENDIX Notation The transition euations used in Tables A1 A6 are comlicated by the addition of gene conversion events. In an attemt to simlify the euations used to build the tables, a new notation, which incororates the notation of McVean (2007), is used. The new notation is outlined and comared to McVean s below. All of the symbols used refer to escae robabilities during the selection hase that result from either recombination or gene conversion. For NSN (Tables A1 A3): McVean (2007): x is used to indicate that no recombination event has occurred between locus X and locus S. x is used to indicate that a recombination event has occurred between locus X and locus S. y is used to indicate that no recombination event has occurred between locus S and locus Y. y is used to indicate that a recombination event has occurred between locus S and locus Y.
6 1256 D. A. Jones and J. Wakeley TABLE A1 Transition robabilities for NSN for the starting configuration A to the four states, A, B,, or O, resent at the beginning of the neutral hase onfiguration at the end of selection hase Probability given starting configuration [i S j S, i S j S ] onfiguration at the start of the neutral hase [i S j S, i S j S ] ( [i S j S, i S k W ] 2( y *)(*(1 g x s) [i S j W, i S k S ] 2( y *)(*(1 g x s) [i S j W, i S k W ] 2( y *)(* x * y x * * g y s 1 x * * g y s) B [i S j S, k W l W ] (( y *))2 O [i W j W, k S l S ] ( [i S j W, k S l W ] 2( y *)(*(1 g x s) [i S j W, k W l W ] 2( y *)(* x * y x * y * g s 1 x * * g y s) [i W j W, k S l W ] 2( x * y * g s 1 x * * g y s)( y *) [i W j W, k W l W ] ( x * * g y s 1 x * * g y s) 2 [i S j S, i S k S ] 0 O [i S j S, k S l W ] 0 O [i S j W, k S l S ] 0 O [i S j S, k S l S ] 0 O [i S j W, i S j W ] 2( y *)(* g x s y *) A [i W j W, i W k S ] 2( y *)(*(1 g x s) [i W j W, i W j W ] ( y *)2 A [i W j S, i W k W ] 2( y *)(*(1 g x s) [i W j W, i W k W ] 2( y *)(* x * y x * * g y s 1 x * * g y s) B [i W j S, i W k S ] 0 A O corresonds to a state where at least one of the two neutral loci has coalesced. There are six new states created by the addition of gene conversion that are not resent when recombination is the only crossingover event otion. These are the six last states. This table corresonds to the transition robabilities in the second column of Aendix A of McVean (2007) and the same notation, exlained in detail in Figure 3, is used. Notation with gene conversion: The terminology is the same as that in McVean (2007) but with the additional consideration that escae from the selection swee can also come from gene conversion. x * ¼ xð1 g x Þ: there is no recombination between locus X and locus S and no gene conversion at locus X. x * ¼ 1 * ¼½ x xð1 g x Þ 1 ð1 x Þg x x g x Š: there is either at least one recombination event between locus X and locus S or at least one gene conversion at locus X. y * ¼ yð1 g y Þ: there is no recombination between locus S and locus Y and no gene conversion at locus Y. y * ¼ 1 * ¼½ y yð1 g y Þ 1 ð1 y Þg y y g y Š: there is either at least one recombination event between locus S and locus Y or at least one gene conversion at locus Y. For SNN (Tables A4 A6): McVean (2007): x and x have the same meaning as their NSN counterarts. y is used to indicate that no recombination event has occurred between locus X and locus Y. y is used to indicate that at least one recombination event has occurred between locus X and locus Y. Notation with gene conversion: x * and x * have the same meaning as their NSN counterarts. y * ¼ yð1 g y Þ: there is no recombination between locus X and locus Y and no gene conversion at locus Y. y * ¼ 1 y * ¼ [ y ð1 g y Þ 1 ð1 y Þg y y g y Š ¼ ½ y 1 g y y g y Š: there is either at least one recombination event between locus X and locus Y or at least one gene conversion at locus Y. There are two additional robabilities resent in the case of SNN: s ¼ (1 x )(1 g s ) is the robability that no recombination event has occurred between locus S and locus X and no gene conversion event has occurred at locus S. s ¼ [ x (1 g s ) 1 (1 x )g s x g s ] ¼ [ x 1 g s x g s ]is the robability that a recombination event between locus S and locus X or a gene conversion event at locus S or both has occurred.
7 Note 1257 TABLE A2 Transition robabilities for NSN for the starting configuration B onfiguration at the end of selection hase Probability given starting configuration [i S j S, i S k S ] onfiguration at start of neutral hase [i S j S, i S j S ] 0 O [i S j S, i S k W ] y * x *(1 g s)( y * y * * g y s) O [i S j W, i S k S ] x *) y *(1 g s) O [i S j W, i S k W ] y *((* 1 x * g x s))( y * g s) B [i S j S, k W l W ] y * * (1 g x s)( y * g s) O [i W j W, k S l S ] x *)*(1 g y s) O [i S j W, k S l W ] ( *(1 g *(* 1 g *)*(1 g *(1 g B x s) y x s x y s) x s) y * * (1 g x s)( y * g s) 1 ( x * * g y s 1 x * * g y s) x *(1 g s) 2 y *) [i S j W, k W l W ] ( y *(* 1 x * g x s)( y * g s) 1 ( 1 [i W j W, k S l W ] x * y s x * y x y y ( *(1 g x s) y *(* 1 x * g x s)( * g y s) 1 ( 1 x * * g y s 1 x * * g y s)( * 1 g x s x *)*(1 g y s) * g 1 * g s) *(1 g s)( * 1 * g s)) [i W j W, k W l W ] ( x * * g y s 1 x * * g y s)( x * 1 * g x s)( y * g s) [i S j S, i S k S ] y * x *(1 g s) 2 [i S j S, k S l W ] y * * (1 g x s) 2 [i S j W, k S l S ] y * * (1 g x s) 2 [i S j S, k S l S ] 0 O [i S j W, i S j W ] 0 A [i W j W, i W k S ] x *) y *(1 g s) B [i W j W, i W j W ] 0 A [i W j S, i W k W ] y * * (1 g x s)( y * g s) B [i W j W, i W k W ] y *(* 1 x * g x s)( y * g s) B [i W j S, i W k S ] y * * (1 g x s) 2 y * A The six new states created by the addition of gene conversion are the last six rows of the table. This corresonds to the transition robabilities in the third column of Aendix A of McVean (2007). onfiguration at the end of selection hase TABLE A3 Transition robabilities for NSN for the starting configuration Probability given starting configuration [i S j S, k S l S ] onfiguration at the start of neutral hase [i S j S, i S j S ] 0 O [i S j S, i S k W ] 0 O [i S j W, i S k S ] 0 O [i S j W, i S k W ] 0 B [i S j S, k W l W ] ( x *(1 g s)( y * g s)) 2 O [i W j W, k S l S ] (( * 1 g s *)*(1 g s)) 2 O [i S j W, k S l W ] x x y 2( x *(1 g s) 2 y *(* 1 x * g x s)( y * g s) 1 x *(1 g s)( y * g s) B ( x * 1 g s x *)*(1 g y s)) x x y x * x y y [i W j W, k W l W ] (( * 1 x * g x s)( * g y s)) 2 [i S j S, i S k S ] 0 O [i S j W, k W l W ] [i W j W, k S l W ] 2 x *(1 g s)( y * g s)( x * 1 * g x s)( y * g s) 2( * 1 g s *) *(1 g s)( * 1 g s)( * 1 * g s) [i S j S, k S l W ] 2 x *(1 g s) 2 y * * (1 g x s)( y * g s) O [i S j W, k S l S ] 2 x *(1 g s) 2 x *)*(1 g y s) O [i S j S, k S l S ] ( x *(1 g s) 2 [i S j W, i S j W ] 0 A [i W j W, i W k S ] 0 B [i W j W, i W j W ] 0 A [i W j S, i W k W ] 0 B [i W j W, i W k W ] 0 B [i W j S, i W k S ] 0 A There are six new states created by the addition of gene conversion; these are described in the last six rows of the table. This corresonds to the transition robabilities in the fourth column of Aendix A of McVean (2007).
8 1258 D. A. Jones and J. Wakeley TABLE A4 Transition robabilities for SNN when the starting configuration is A onfiguration at the end of selection hase Probability given starting configuration [i S j S, i S j S ] onfiguration at the start of neutral hase [i S j S, i S j S ] ( x * y *(1 g s)) 2 O [i S j W, i S j W ] 2 x * * (1 g y s)( x 1 g s x g s )(1 g x ) y * A [i S j S, i S k W ] 2( x * * (1 g y s))( [i S j W, i S k W ] 2 x * * (1 g y s)((g s x x g s )( [i W j W, i W j W ] (( x 1 g s x g s )(1 g x ) y *)2 A [i W j S, i W k W ] 2( x 1 g s x g s )(1 g x ) y * * (1 g x s) y * B [i S j S, k W l W ] ( [i S j W, k W l W ] 2 y *((g s x x g s )( y *) [i W j W, i W k W ] 2( x 1 g s x g s )(1 g x ) y *((g s x x g s )( [i W j W, k W l W ] (((g s x x g s )( y *))2 [i S j S, i S k S ] 0 O [i S j W, i S k S ] 2( x * * (1 g y s))( [i S j S, k S l W ] 0 O [i W j S, i W k S ] 0 A [i S j W, k S l W ] 2 y * y * B [i W j W, i W k S ] 2( x 1 g s x g s )(1 g x ) y * * g s x y * B [i W j W, k S l W ] 2 y *((g s x x g s )( y *) [i S j W, k S l S ] 0 O [i W j W, k S l S ] ( [i S j S, k S l S ] 0 O There are no new states created with the addition of gene conversion but there are new transition robabilities. This table corresonds to the transition robabilities in the second column of Aendix B of McVean (2007). TABLE A5 Transition robabilities for SNN when the starting configuration is B onfiguration at the end of selection hase Probability given starting configuration [i S j S, i S k S ] onfiguration at the start of neutral hase [i S j S, i S j S ] 0 O [i S j W, i S j W ] 0 A [i S j S, i S k W ] ( x * * (1 g y s))( x *(1 g s))( y * xg s 1 x y g y x y 1 x y x y ) O [i S j W, i S k W ] x * * (1 g y s)( x * 1 (1 g x)g s (1 x ))( y * xg s 1 x y g y x y 1 x y x y ) B [i W j W, i W j W ] 0 A [i W j S, i W k W ] ( x 1 g s x g s )(1 g x ) y *(*(1 g x s))( y * xg s 1 x y g y x y 1 x y x y ) B [i S j S, k W l W ] y *( x *(1 g s))( y * xg s 1 x y g y x y 1 x y x y ) O [i S j W, k W l W ] y *(* 1 (1 g x x)g s (1 x ))( y * xg s 1 x y g y x y 1 x y x y ) 1 ((g s x x g s )( y *)(*(1 g x s))( y * xg s 1 x y g y 1 x y 1 x y x y ) [i W j W, i W k W ] ( x 1 g s x g s )(1 g x ) y *(* 1 (1 g x x)g s (1 x )) B ( y * xg s 1 x y g y x y 1 x y x y ) [i W j W, k W l W ] ((g s x x g s )( y * g x)1 x (1 g s )g x y *)(* 1 (1 g x x)g s (1 x ))( y * xg s 1 x y g y x y 1 x y x y ) [i S j S, i S k S ] x * * (1 g y s) x * x(1 g s ) 2 [i S j W, i S k S ] ( x * * (1 g y s))( x * 1 g s [i S j S, k S l W ] y * * x x(1 g s ) 2 [i W j S, i W k S ] ( x 1 g s x g s )(1 g x ) y * x * x(1 g s ) 2 y * A [i S j W, k S l W ] y *(*(1 g x s))( y * xg s 1 x y g y x y 1 x y x y )1 x *(1 g s) B y *)1 ((g s x x g s )( y * g x) 1 x (1 g s )g x y *)* x x(1 g s ) 2 y * [i W j W, i W k S ] ( x 1 g s x g s )(1 g x ) [i W j W, k S l W ] y *(* 1 (1 g x x)g s (1 x ))( y * xg s 1 x y g y x y 1 x y x y )1 ((g s x x g s )( y *)(* 1 g x s y *) [i S j W, k S l S ] y * * x x(1 g s ) 2 [i W j W, k S l S ] [i S j S, k S l S ] 0 O There are no new states created with the addition of gene conversion but there are no new transition robabilities. This corresonds to the transition robabilities in the third column of Aendix B of McVean (2007).
9 Note 1259 TABLE A6 Transition robabilities for SNN when the starting configuration is onfiguration at the end of selection hase Probability given starting configuration [i S j S, k S l S ] onfiguration at the start of neutral hase [i S j S, i S j S ] 0 O [i S j W, i S j W ] 0 A [i S j S, i S k W ] 0 O [i S j W, i S k W ] 0 B [i W j W, i W j W ] 0 A [i W j S, i W k W ] 0 B [i S j S, k W l W ] (( x *(1 g s))( y * xg s 1 x y g y x y 1 x y x y )) 2 O [i S j W, k W l W ] 2( x *(1 g s))( y * xg s 1 x y g y x y 1 x y x y )( x * 1 (1 g x) g s (1 x ))*( y * xg s 1 x y g y x y 1 x y x y ) [i W j W, i W k W ] 0 B [i W j W, k W l W ] ( x * 1 (1 g x)g s (1 x ))( y * xg s 1 x y g y x y 1 x y x y )) 2 [i S j S, i S k S ] 0 O [i S j W, i S k S ] 0 O [i S j S, k S l W ] 2 x * x(1 g s ) 2 y *(*(1 g x s))( y * xg s 1 x y g y x y 1 x y x y ) O [i W j S, i W k S ] 0 A [i S j W, k S l W ] 2( x *(1 g s))( y * xg s 1 x y g y x y 1 x y x y )( x * 1 g s x *) B ( x (1 g s ) y *)1 2* x x(1 g s ) 2 y *(* 1 (1 g x x)g s (1 x )) ( y * xg s 1 x y g y x y 1 x y x y ) [i W j W, i W k S ] 0 B [i W j W, k S l W ] 2( x * 1 g s y *)(* 1 (1 g x x)g s (1 x ))( y * xg s 1 x y g y 1 x y 1 x y x y ) [i S j W, k S l S ] 2 x * x(1 g s ) 2 y * [i W j W, k S l S ] (( x * 1 g s y *))2 O [i S j S, k S l S ] ( x * x(1 g s ) 2 There are no new states created with the addition of gene conversion but there are new transition robabilities. This corresonds to the transition robabilities in the fourth column of Aendix B of McVean (2007).
Population Genetics II (Selection + Haplotype analyses)
26 th Oct 2015 Poulation Genetics II (Selection + Halotye analyses) Gurinder Singh Mickey twal Center for Quantitative iology Natural Selection Model (Molecular Evolution) llele frequency Embryos Selection
More informationGenetic variation of polygenic characters and the evolution of genetic degeneracy
Genetic variation of olygenic characters and the evolution of genetic degeneracy S. A. FRANK Deartment of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA Keywords: mutation;
More informationA Genealogical Interpretation of Principal Components Analysis
A Genealogical Interretation of Princial Comonents Analysis Gil McVean* Deartment of Statistics, University of Oxford, Oxford, United Kingdom Abstract Princial comonents analysis, PCA, is a statistical
More informationOutline. Markov Chains and Markov Models. Outline. Markov Chains. Markov Chains Definitions Huizhen Yu
and Markov Models Huizhen Yu janey.yu@cs.helsinki.fi Det. Comuter Science, Univ. of Helsinki Some Proerties of Probabilistic Models, Sring, 200 Huizhen Yu (U.H.) and Markov Models Jan. 2 / 32 Huizhen Yu
More informationScaling Multiple Point Statistics for Non-Stationary Geostatistical Modeling
Scaling Multile Point Statistics or Non-Stationary Geostatistical Modeling Julián M. Ortiz, Steven Lyster and Clayton V. Deutsch Centre or Comutational Geostatistics Deartment o Civil & Environmental Engineering
More informationarxiv: v1 [physics.data-an] 26 Oct 2012
Constraints on Yield Parameters in Extended Maximum Likelihood Fits Till Moritz Karbach a, Maximilian Schlu b a TU Dortmund, Germany, moritz.karbach@cern.ch b TU Dortmund, Germany, maximilian.schlu@cern.ch
More informationarxiv: v2 [q-bio.pe] 5 Jan 2018
Phenotyic switching of oulations of cells in a stochastic environment arxiv:76.7789v [-bio.pe] 5 Jan 8 Peter G. Hufton,, Yen Ting Lin,,, and Tobias Galla, Theoretical Physics, School of Physics and stronomy,
More informationNUMERICAL AND THEORETICAL INVESTIGATIONS ON DETONATION- INERT CONFINEMENT INTERACTIONS
NUMERICAL AND THEORETICAL INVESTIGATIONS ON DETONATION- INERT CONFINEMENT INTERACTIONS Tariq D. Aslam and John B. Bdzil Los Alamos National Laboratory Los Alamos, NM 87545 hone: 1-55-667-1367, fax: 1-55-667-6372
More informationA Comparison between Biased and Unbiased Estimators in Ordinary Least Squares Regression
Journal of Modern Alied Statistical Methods Volume Issue Article 7 --03 A Comarison between Biased and Unbiased Estimators in Ordinary Least Squares Regression Ghadban Khalaf King Khalid University, Saudi
More informationAn Analysis of TCP over Random Access Satellite Links
An Analysis of over Random Access Satellite Links Chunmei Liu and Eytan Modiano Massachusetts Institute of Technology Cambridge, MA 0239 Email: mayliu, modiano@mit.edu Abstract This aer analyzes the erformance
More informationOrdered and disordered dynamics in random networks
EUROPHYSICS LETTERS 5 March 998 Eurohys. Lett., 4 (6),. 599-604 (998) Ordered and disordered dynamics in random networks L. Glass ( )andc. Hill 2,3 Deartment of Physiology, McGill University 3655 Drummond
More informationSession 5: Review of Classical Astrodynamics
Session 5: Review of Classical Astrodynamics In revious lectures we described in detail the rocess to find the otimal secific imulse for a articular situation. Among the mission requirements that serve
More informationState Estimation with ARMarkov Models
Deartment of Mechanical and Aerosace Engineering Technical Reort No. 3046, October 1998. Princeton University, Princeton, NJ. State Estimation with ARMarkov Models Ryoung K. Lim 1 Columbia University,
More informationDomain Dynamics in a Ferroelastic Epilayer on a Paraelastic Substrate
Y. F. Gao Z. Suo Mechanical and Aerosace Engineering Deartment and Princeton Materials Institute, Princeton University, Princeton, NJ 08544 Domain Dynamics in a Ferroelastic Eilayer on a Paraelastic Substrate
More informationAn Outdoor Recreation Use Model with Applications to Evaluating Survey Estimators
United States Deartment of Agriculture Forest Service Southern Research Station An Outdoor Recreation Use Model with Alications to Evaluating Survey Estimators Stanley J. Zarnoch, Donald B.K. English,
More informationTests for Two Proportions in a Stratified Design (Cochran/Mantel-Haenszel Test)
Chater 225 Tests for Two Proortions in a Stratified Design (Cochran/Mantel-Haenszel Test) Introduction In a stratified design, the subects are selected from two or more strata which are formed from imortant
More informationp(d g A,g B )p(g B ), g B
Supplementary Note Marginal effects for two-locus models Here we derive the marginal effect size of the three models given in Figure 1 of the main text. For each model we assume the two loci (A and B)
More informationSTABILITY ANALYSIS AND CONTROL OF STOCHASTIC DYNAMIC SYSTEMS USING POLYNOMIAL CHAOS. A Dissertation JAMES ROBERT FISHER
STABILITY ANALYSIS AND CONTROL OF STOCHASTIC DYNAMIC SYSTEMS USING POLYNOMIAL CHAOS A Dissertation by JAMES ROBERT FISHER Submitted to the Office of Graduate Studies of Texas A&M University in artial fulfillment
More informationA Game Theoretic Investigation of Selection Methods in Two Population Coevolution
A Game Theoretic Investigation of Selection Methods in Two Poulation Coevolution Sevan G. Ficici Division of Engineering and Alied Sciences Harvard University Cambridge, Massachusetts 238 USA sevan@eecs.harvard.edu
More informationTHERE is a growing body of experimental evidence
Coyright Ó 2008 by the Genetics Society of America DOI: 10.1534/genetics.107.082610 Waiting for Two Mutations: With Alications to Regulatory Sequence Evolution and the Limits of Darwinian Evolution Rick
More informationCHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules
CHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules. Introduction: The is widely used in industry to monitor the number of fraction nonconforming units. A nonconforming unit is
More informationStatistical Tests for Detecting Positive Selection by Utilizing High. Frequency SNPs
Statistical Tests for Detecting Positive Selection by Utilizing High Frequency SNPs Kai Zeng *, Suhua Shi Yunxin Fu, Chung-I Wu * * Department of Ecology and Evolution, University of Chicago, Chicago,
More informationInformation collection on a graph
Information collection on a grah Ilya O. Ryzhov Warren Powell October 25, 2009 Abstract We derive a knowledge gradient olicy for an otimal learning roblem on a grah, in which we use sequential measurements
More informationEffective conductivity in a lattice model for binary disordered media with complex distributions of grain sizes
hys. stat. sol. b 36, 65-633 003 Effective conductivity in a lattice model for binary disordered media with comlex distributions of grain sizes R. PIASECKI Institute of Chemistry, University of Oole, Oleska
More informationMaximum Entropy and the Stress Distribution in Soft Disk Packings Above Jamming
Maximum Entroy and the Stress Distribution in Soft Disk Packings Above Jamming Yegang Wu and S. Teitel Deartment of Physics and Astronomy, University of ochester, ochester, New York 467, USA (Dated: August
More information1 Random Experiments from Random Experiments
Random Exeriments from Random Exeriments. Bernoulli Trials The simlest tye of random exeriment is called a Bernoulli trial. A Bernoulli trial is a random exeriment that has only two ossible outcomes: success
More informationDeveloping A Deterioration Probabilistic Model for Rail Wear
International Journal of Traffic and Transortation Engineering 2012, 1(2): 13-18 DOI: 10.5923/j.ijtte.20120102.02 Develoing A Deterioration Probabilistic Model for Rail Wear Jabbar-Ali Zakeri *, Shahrbanoo
More informationMODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL
Technical Sciences and Alied Mathematics MODELING THE RELIABILITY OF CISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Cezar VASILESCU Regional Deartment of Defense Resources Management
More informationUsing a Computational Intelligence Hybrid Approach to Recognize the Faults of Variance Shifts for a Manufacturing Process
Journal of Industrial and Intelligent Information Vol. 4, No. 2, March 26 Using a Comutational Intelligence Hybrid Aroach to Recognize the Faults of Variance hifts for a Manufacturing Process Yuehjen E.
More informationActive and reactive power control schemes for distributed generation systems under voltage dips Wang, F.; Duarte, J.L.; Hendrix, M.A.M.
Active and reactive ower control schemes for distributed generation systems under voltage dis Wang, F.; Duarte, J.L.; Hendrix, M.A.M. ublished in: roceedings IEEE Energy Conversion Congress and Exosition
More informationPHYSICAL REVIEW LETTERS
PHYSICAL REVIEW LETTERS VOLUME 81 20 JULY 1998 NUMBER 3 Searated-Path Ramsey Atom Interferometer P. D. Featonby, G. S. Summy, C. L. Webb, R. M. Godun, M. K. Oberthaler, A. C. Wilson, C. J. Foot, and K.
More informationUse of Transformations and the Repeated Statement in PROC GLM in SAS Ed Stanek
Use of Transformations and the Reeated Statement in PROC GLM in SAS Ed Stanek Introduction We describe how the Reeated Statement in PROC GLM in SAS transforms the data to rovide tests of hyotheses of interest.
More informationGenetic Algorithms, Selection Schemes, and the Varying Eects of Noise. IlliGAL Report No November Department of General Engineering
Genetic Algorithms, Selection Schemes, and the Varying Eects of Noise Brad L. Miller Det. of Comuter Science University of Illinois at Urbana-Chamaign David E. Goldberg Det. of General Engineering University
More informationNotes on Instrumental Variables Methods
Notes on Instrumental Variables Methods Michele Pellizzari IGIER-Bocconi, IZA and frdb 1 The Instrumental Variable Estimator Instrumental variable estimation is the classical solution to the roblem of
More informationPeriod-two cycles in a feedforward layered neural network model with symmetric sequence processing
PHYSICAL REVIEW E 75, 4197 27 Period-two cycles in a feedforward layered neural network model with symmetric sequence rocessing F. L. Metz and W. K. Theumann Instituto de Física, Universidade Federal do
More information7.2 Inference for comparing means of two populations where the samples are independent
Objectives 7.2 Inference for comaring means of two oulations where the samles are indeendent Two-samle t significance test (we give three examles) Two-samle t confidence interval htt://onlinestatbook.com/2/tests_of_means/difference_means.ht
More informationInformation collection on a graph
Information collection on a grah Ilya O. Ryzhov Warren Powell February 10, 2010 Abstract We derive a knowledge gradient olicy for an otimal learning roblem on a grah, in which we use sequential measurements
More informationsubstantial literature on emirical likelihood indicating that it is widely viewed as a desirable and natural aroach to statistical inference in a vari
Condence tubes for multile quantile lots via emirical likelihood John H.J. Einmahl Eindhoven University of Technology Ian W. McKeague Florida State University May 7, 998 Abstract The nonarametric emirical
More information8 STOCHASTIC PROCESSES
8 STOCHASTIC PROCESSES The word stochastic is derived from the Greek στoχαστικoς, meaning to aim at a target. Stochastic rocesses involve state which changes in a random way. A Markov rocess is a articular
More informationarxiv: v1 [stat.ml] 10 Mar 2016
Global and Local Uncertainty Princiles for Signals on Grahs Nathanael Perraudin, Benjamin Ricaud, David I Shuman, and Pierre Vandergheynst March, 206 arxiv:603.03030v [stat.ml] 0 Mar 206 Abstract Uncertainty
More informationChurilova Maria Saint-Petersburg State Polytechnical University Department of Applied Mathematics
Churilova Maria Saint-Petersburg State Polytechnical University Deartment of Alied Mathematics Technology of EHIS (staming) alied to roduction of automotive arts The roblem described in this reort originated
More information#A64 INTEGERS 18 (2018) APPLYING MODULAR ARITHMETIC TO DIOPHANTINE EQUATIONS
#A64 INTEGERS 18 (2018) APPLYING MODULAR ARITHMETIC TO DIOPHANTINE EQUATIONS Ramy F. Taki ElDin Physics and Engineering Mathematics Deartment, Faculty of Engineering, Ain Shams University, Cairo, Egyt
More informationrate~ If no additional source of holes were present, the excess
DIFFUSION OF CARRIERS Diffusion currents are resent in semiconductor devices which generate a satially non-uniform distribution of carriers. The most imortant examles are the -n junction and the biolar
More informationClassical gas (molecules) Phonon gas Number fixed Population depends on frequency of mode and temperature: 1. For each particle. For an N-particle gas
Lecture 14: Thermal conductivity Review: honons as articles In chater 5, we have been considering quantized waves in solids to be articles and this becomes very imortant when we discuss thermal conductivity.
More informationCombining Logistic Regression with Kriging for Mapping the Risk of Occurrence of Unexploded Ordnance (UXO)
Combining Logistic Regression with Kriging for Maing the Risk of Occurrence of Unexloded Ordnance (UXO) H. Saito (), P. Goovaerts (), S. A. McKenna (2) Environmental and Water Resources Engineering, Deartment
More informationarxiv:cond-mat/ v2 25 Sep 2002
Energy fluctuations at the multicritical oint in two-dimensional sin glasses arxiv:cond-mat/0207694 v2 25 Se 2002 1. Introduction Hidetoshi Nishimori, Cyril Falvo and Yukiyasu Ozeki Deartment of Physics,
More informationLending Direction to Neural Networks. Richard S. Zemel North Torrey Pines Rd. La Jolla, CA Christopher K. I.
Lending Direction to Neural Networks Richard S Zemel CNL, The Salk Institute 10010 North Torrey Pines Rd La Jolla, CA 92037 Christoher K I Williams Deartment of Comuter Science University of Toronto Toronto,
More informationCharacteristics of Beam-Based Flexure Modules
Shorya Awtar e-mail: shorya@mit.edu Alexander H. Slocum e-mail: slocum@mit.edu Precision Engineering Research Grou, Massachusetts Institute of Technology, Cambridge, MA 039 Edi Sevincer Omega Advanced
More informationCHAPTER 5 STATISTICAL INFERENCE. 1.0 Hypothesis Testing. 2.0 Decision Errors. 3.0 How a Hypothesis is Tested. 4.0 Test for Goodness of Fit
Chater 5 Statistical Inference 69 CHAPTER 5 STATISTICAL INFERENCE.0 Hyothesis Testing.0 Decision Errors 3.0 How a Hyothesis is Tested 4.0 Test for Goodness of Fit 5.0 Inferences about Two Means It ain't
More informationStatics and dynamics: some elementary concepts
1 Statics and dynamics: some elementary concets Dynamics is the study of the movement through time of variables such as heartbeat, temerature, secies oulation, voltage, roduction, emloyment, rices and
More informationPERFORMANCE BASED DESIGN SYSTEM FOR CONCRETE MIXTURE WITH MULTI-OPTIMIZING GENETIC ALGORITHM
PERFORMANCE BASED DESIGN SYSTEM FOR CONCRETE MIXTURE WITH MULTI-OPTIMIZING GENETIC ALGORITHM Takafumi Noguchi 1, Iei Maruyama 1 and Manabu Kanematsu 1 1 Deartment of Architecture, University of Tokyo,
More informationSWEEPFINDER2: Increased sensitivity, robustness, and flexibility
SWEEPFINDER2: Increased sensitivity, robustness, and flexibility Michael DeGiorgio 1,*, Christian D. Huber 2, Melissa J. Hubisz 3, Ines Hellmann 4, and Rasmus Nielsen 5 1 Department of Biology, Pennsylvania
More informationProspect Theory Explains Newsvendor Behavior: The Role of Reference Points
Submitted to Management Science manuscrit (Please, rovide the mansucrit number! Authors are encouraged to submit new aers to INFORMS journals by means of a style file temlate, which includes the journal
More informationModeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlation
6.3 Modeling and Estimation of Full-Chi Leaage Current Considering Within-Die Correlation Khaled R. eloue, Navid Azizi, Farid N. Najm Deartment of ECE, University of Toronto,Toronto, Ontario, Canada {haled,nazizi,najm}@eecg.utoronto.ca
More informationEXACTLY PERIODIC SUBSPACE DECOMPOSITION BASED APPROACH FOR IDENTIFYING TANDEM REPEATS IN DNA SEQUENCES
EXACTLY ERIODIC SUBSACE DECOMOSITION BASED AROACH FOR IDENTIFYING TANDEM REEATS IN DNA SEUENCES Ravi Guta, Divya Sarthi, Ankush Mittal, and Kuldi Singh Deartment of Electronics & Comuter Engineering, Indian
More informationUsing the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process
Using the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process P. Mantalos a1, K. Mattheou b, A. Karagrigoriou b a.deartment of Statistics University of Lund
More informationA MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST BASED ON THE WEIBULL DISTRIBUTION
O P E R A T I O N S R E S E A R C H A N D D E C I S I O N S No. 27 DOI:.5277/ord73 Nasrullah KHAN Muhammad ASLAM 2 Kyung-Jun KIM 3 Chi-Hyuck JUN 4 A MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST
More informationdn i where we have used the Gibbs equation for the Gibbs energy and the definition of chemical potential
Chem 467 Sulement to Lectures 33 Phase Equilibrium Chemical Potential Revisited We introduced the chemical otential as the conjugate variable to amount. Briefly reviewing, the total Gibbs energy of a system
More informationYixi Shi. Jose Blanchet. IEOR Department Columbia University New York, NY 10027, USA. IEOR Department Columbia University New York, NY 10027, USA
Proceedings of the 2011 Winter Simulation Conference S. Jain, R. R. Creasey, J. Himmelsach, K. P. White, and M. Fu, eds. EFFICIENT RARE EVENT SIMULATION FOR HEAVY-TAILED SYSTEMS VIA CROSS ENTROPY Jose
More informationPellet fuelling of plasmas with ELM mitigation by resonant magnetic perturbations in MAST
1 Pellet fuelling of lasmas with mitigation by resonant magnetic erturbations in MAST M Valovič, G Cunningham, L Garzotti, C Gurl, A Kirk, G Naylor, A Patel, R Scannell, A J Thornton and the MAST team
More informationAn Improved Generalized Estimation Procedure of Current Population Mean in Two-Occasion Successive Sampling
Journal of Modern Alied Statistical Methods Volume 15 Issue Article 14 11-1-016 An Imroved Generalized Estimation Procedure of Current Poulation Mean in Two-Occasion Successive Samling G. N. Singh Indian
More informationAn Improved Calibration Method for a Chopped Pyrgeometer
96 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 17 An Imroved Calibration Method for a Choed Pyrgeometer FRIEDRICH FERGG OtoLab, Ingenieurbüro, Munich, Germany PETER WENDLING Deutsches Forschungszentrum
More informationMATHEMATICAL MODELLING OF THE WIRELESS COMMUNICATION NETWORK
Comuter Modelling and ew Technologies, 5, Vol.9, o., 3-39 Transort and Telecommunication Institute, Lomonosov, LV-9, Riga, Latvia MATHEMATICAL MODELLIG OF THE WIRELESS COMMUICATIO ETWORK M. KOPEETSK Deartment
More informationSIMULATION OF DIFFUSION PROCESSES IN LABYRINTHIC DOMAINS BY USING CELLULAR AUTOMATA
SIMULATION OF DIFFUSION PROCESSES IN LABYRINTHIC DOMAINS BY USING CELLULAR AUTOMATA Udo Buschmann and Thorsten Rankel and Wolfgang Wiechert Deartment of Simulation University of Siegen Paul-Bonatz-Str.
More informationChapter 1: PROBABILITY BASICS
Charles Boncelet, obability, Statistics, and Random Signals," Oxford University ess, 0. ISBN: 978-0-9-0005-0 Chater : PROBABILITY BASICS Sections. What Is obability?. Exeriments, Outcomes, and Events.
More informationPressure-sensitivity Effects on Toughness Measurements of Compact Tension Specimens for Strain-hardening Solids
American Journal of Alied Sciences (9): 19-195, 5 ISSN 1546-939 5 Science Publications Pressure-sensitivity Effects on Toughness Measurements of Comact Tension Secimens for Strain-hardening Solids Abdulhamid
More informationpp physics, RWTH, WS 2003/04, T.Hebbeker
1. PP TH 03/04 Accelerators and Detectors 1 hysics, RWTH, WS 2003/04, T.Hebbeker 2003-12-03 1. Accelerators and Detectors In the following, we concentrate on the three machines SPS, Tevatron and LHC with
More informationVoting with Behavioral Heterogeneity
Voting with Behavioral Heterogeneity Youzong Xu Setember 22, 2016 Abstract This aer studies collective decisions made by behaviorally heterogeneous voters with asymmetric information. Here behavioral heterogeneity
More informationt 0 Xt sup X t p c p inf t 0
SHARP MAXIMAL L -ESTIMATES FOR MARTINGALES RODRIGO BAÑUELOS AND ADAM OSȨKOWSKI ABSTRACT. Let X be a suermartingale starting from 0 which has only nonnegative jums. For each 0 < < we determine the best
More informationPretest (Optional) Use as an additional pacing tool to guide instruction. August 21
Trimester 1 Pretest (Otional) Use as an additional acing tool to guide instruction. August 21 Beyond the Basic Facts In Trimester 1, Grade 8 focus on multilication. Daily Unit 1: Rational vs. Irrational
More informationA Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split
A Bound on the Error of Cross Validation Using the Aroximation and Estimation Rates, with Consequences for the Training-Test Slit Michael Kearns AT&T Bell Laboratories Murray Hill, NJ 7974 mkearns@research.att.com
More informationPaper C Exact Volume Balance Versus Exact Mass Balance in Compositional Reservoir Simulation
Paer C Exact Volume Balance Versus Exact Mass Balance in Comositional Reservoir Simulation Submitted to Comutational Geosciences, December 2005. Exact Volume Balance Versus Exact Mass Balance in Comositional
More informationUncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
TNN-2009-P-1186.R2 1 Uncorrelated Multilinear Princial Comonent Analysis for Unsuervised Multilinear Subsace Learning Haiing Lu, K. N. Plataniotis and A. N. Venetsanooulos The Edward S. Rogers Sr. Deartment
More informationEvaluating Process Capability Indices for some Quality Characteristics of a Manufacturing Process
Journal of Statistical and Econometric Methods, vol., no.3, 013, 105-114 ISSN: 051-5057 (rint version), 051-5065(online) Scienress Ltd, 013 Evaluating Process aability Indices for some Quality haracteristics
More informationTemperature, current and doping dependence of non-ideality factor for pnp and npn punch-through structures
Indian Journal of Pure & Alied Physics Vol. 44, December 2006,. 953-958 Temerature, current and doing deendence of non-ideality factor for n and nn unch-through structures Khurshed Ahmad Shah & S S Islam
More informationKEY ISSUES IN THE ANALYSIS OF PILES IN LIQUEFYING SOILS
4 th International Conference on Earthquake Geotechnical Engineering June 2-28, 27 KEY ISSUES IN THE ANALYSIS OF PILES IN LIQUEFYING SOILS Misko CUBRINOVSKI 1, Hayden BOWEN 1 ABSTRACT Two methods for analysis
More informationA Closed-Form Solution to the Minimum V 2
Celestial Mechanics and Dynamical Astronomy manuscrit No. (will be inserted by the editor) Martín Avendaño Daniele Mortari A Closed-Form Solution to the Minimum V tot Lambert s Problem Received: Month
More informationHidden Predictors: A Factor Analysis Primer
Hidden Predictors: A Factor Analysis Primer Ryan C Sanchez Western Washington University Factor Analysis is a owerful statistical method in the modern research sychologist s toolbag When used roerly, factor
More informationBayesian inference & Markov chain Monte Carlo. Note 1: Many slides for this lecture were kindly provided by Paul Lewis and Mark Holder
Bayesian inference & Markov chain Monte Carlo Note 1: Many slides for this lecture were kindly rovided by Paul Lewis and Mark Holder Note 2: Paul Lewis has written nice software for demonstrating Markov
More informationMULTIVARIATE SHEWHART QUALITY CONTROL FOR STANDARD DEVIATION
MULTIVARIATE SHEWHART QUALITY CONTROL FOR STANDARD DEVIATION M. Jabbari Nooghabi, Deartment of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad-Iran. and H. Jabbari
More informationA General Damage Initiation and Evolution Model (DIEM) in LS-DYNA
9th Euroean LS-YNA Conference 23 A General amage Initiation and Evolution Model (IEM) in LS-YNA Thomas Borrvall, Thomas Johansson and Mikael Schill, YNAmore Nordic AB Johan Jergéus, Volvo Car Cororation
More informationarxiv: v2 [q-bio.nc] 14 Sep 2017
Frequency-based brain networks: From a multilex framework to a full multilayer descrition J. M. Buldú,,, and Mason A. Porter Laboratory of Biological Networks, Center for Biomedical Technology (UPM), 8,
More informationOne-way ANOVA Inference for one-way ANOVA
One-way ANOVA Inference for one-way ANOVA IPS Chater 12.1 2009 W.H. Freeman and Comany Objectives (IPS Chater 12.1) Inference for one-way ANOVA Comaring means The two-samle t statistic An overview of ANOVA
More informationECE 534 Information Theory - Midterm 2
ECE 534 Information Theory - Midterm Nov.4, 009. 3:30-4:45 in LH03. You will be given the full class time: 75 minutes. Use it wisely! Many of the roblems have short answers; try to find shortcuts. You
More informationarxiv: v3 [physics.data-an] 23 May 2011
Date: October, 8 arxiv:.7v [hysics.data-an] May -values for Model Evaluation F. Beaujean, A. Caldwell, D. Kollár, K. Kröninger Max-Planck-Institut für Physik, München, Germany CERN, Geneva, Switzerland
More informationLower Confidence Bound for Process-Yield Index S pk with Autocorrelated Process Data
Quality Technology & Quantitative Management Vol. 1, No.,. 51-65, 15 QTQM IAQM 15 Lower onfidence Bound for Process-Yield Index with Autocorrelated Process Data Fu-Kwun Wang * and Yeneneh Tamirat Deartment
More informationChaotic Balanced State in a Model of Cortical Circuits
ARTICLE Communicated by Misha Tsodyks Chaotic Balanced State in a Model of Cortical Circuits C. van Vreeswijk H. Somolinsky Racah Institute of Physics and Center for Neural Comutation, Hebrew University,
More informationA Qualitative Event-based Approach to Multiple Fault Diagnosis in Continuous Systems using Structural Model Decomposition
A Qualitative Event-based Aroach to Multile Fault Diagnosis in Continuous Systems using Structural Model Decomosition Matthew J. Daigle a,,, Anibal Bregon b,, Xenofon Koutsoukos c, Gautam Biswas c, Belarmino
More informationSTART Selected Topics in Assurance
START Selected Toics in Assurance Related Technologies Table of Contents Introduction Statistical Models for Simle Systems (U/Down) and Interretation Markov Models for Simle Systems (U/Down) and Interretation
More informationALTERED LUMINOSITY FUNCTIONS OF RELATIVISTICALLY BEAMED JET POPULATIONS M. L. Lister 1, 2
The Astrohysical Journal, 599:05 5, 003 December 0 # 003. The American Astronomical Society. All rights reserved. Printed in U.S.A. E ALTERED LUMINOSITY FUNCTIONS OF RELATIVISTICALLY BEAMED JET POPULATIONS
More informationIntroduction to Probability and Statistics
Introduction to Probability and Statistics Chater 8 Ammar M. Sarhan, asarhan@mathstat.dal.ca Deartment of Mathematics and Statistics, Dalhousie University Fall Semester 28 Chater 8 Tests of Hyotheses Based
More informationCalculation of MTTF values with Markov Models for Safety Instrumented Systems
7th WEA International Conference on APPLIE COMPUTE CIENCE, Venice, Italy, November -3, 7 3 Calculation of MTTF values with Markov Models for afety Instrumented ystems BÖCÖK J., UGLJEA E., MACHMU. University
More informationD. Gordon, M.A. Levenstien, S.J. Finch, J. Ott. Pacific Symposium on Biocomputing 8: (2003)
Errors and Linkage Disequilibrium Interact Multilicatively Wen Comuting Samle Sizes for Genetic Case-Control Association Studies D. Gordon, M.A. Levenstien, S.J. Finc, J. Ott Pacific Symosium on Biocomuting
More informationRUN-TO-RUN CONTROL AND PERFORMANCE MONITORING OF OVERLAY IN SEMICONDUCTOR MANUFACTURING. 3 Department of Chemical Engineering
Coyright 2002 IFAC 15th Triennial World Congress, Barcelona, Sain RUN-TO-RUN CONTROL AND PERFORMANCE MONITORING OF OVERLAY IN SEMICONDUCTOR MANUFACTURING C.A. Bode 1, B.S. Ko 2, and T.F. Edgar 3 1 Advanced
More informationChapter 7 Rational and Irrational Numbers
Chater 7 Rational and Irrational Numbers In this chater we first review the real line model for numbers, as discussed in Chater 2 of seventh grade, by recalling how the integers and then the rational numbers
More informationHotelling s Two- Sample T 2
Chater 600 Hotelling s Two- Samle T Introduction This module calculates ower for the Hotelling s two-grou, T-squared (T) test statistic. Hotelling s T is an extension of the univariate two-samle t-test
More informationMATH 250: THE DISTRIBUTION OF PRIMES. ζ(s) = n s,
MATH 50: THE DISTRIBUTION OF PRIMES ROBERT J. LEMKE OLIVER For s R, define the function ζs) by. Euler s work on rimes ζs) = which converges if s > and diverges if s. In fact, though we will not exloit
More informationA compression line for soils with evolving particle and pore size distributions due to particle crushing
Russell, A. R. (2011) Géotechnique Letters 1, 5 9, htt://dx.doi.org/10.1680/geolett.10.00003 A comression line for soils with evolving article and ore size distributions due to article crushing A. R. RUSSELL*
More information97.398*, Physical Electronics, Lecture 8. Diode Operation
97.398*, Physical Electronics, Lecture 8 Diode Oeration Lecture Outline Have looked at basic diode rocessing and structures Goal is now to understand and model the behavior of the device under bias First
More informationOn split sample and randomized confidence intervals for binomial proportions
On slit samle and randomized confidence intervals for binomial roortions Måns Thulin Deartment of Mathematics, Usala University arxiv:1402.6536v1 [stat.me] 26 Feb 2014 Abstract Slit samle methods have
More information