Flat-histogram methods in quantum Monte Carlo simulations: Application to the t-j model
|
|
- Jennifer Briggs
- 5 years ago
- Views:
Transcription
1 Joural of Physics: Coferece Series PAPER OPEN ACCESS Flat-histogram methods i quatum Mote Carlo simulatios: Applicatio to the t-j model To cite this article: Nikolaos G. Diamatis ad Efstratios Maousakis 216 J. Phys.: Cof. Ser View the article olie for updates ad ehacemets. Related cotet - Optimized broad-histogram esembles for the simulatio of quatum systems Stefa Wessel, Norbert Stoop, Emauel Gull et al. - Dyamics of the Wag Ladau algorithm ad complexity of rare evets for the 3D bimodalisig spi glass Simo Alder, Simo Trebst, Alexader K Hartma et al. - Mote Carlo calculatios of the freeeergy ladscape of vesicle formatio ad growth S.-J. Zhao ad J. T. Kidt This cotet was dowloaded from IP address o 1/7/218 at :42
2 Flat-histogram methods i quatum Mote Carlo simulatios: Applicatio to the t-j model Nikolaos G. Diamatis (1) ad Efstratios Maousakis (1,2) (1) Departmet of Physics, Uiversity of Athes, Paepistimioupolis, Zografos, Athes, Greece (2) Departmet of Physics ad Natioal High Magetic Field Laboratory, Florida State Uiversity, Tallahassee, FL , USA maousakis@maget.fsu.edu Abstract. We discuss that flat-histogram techiques ca be appropriately applied i the samplig of quatum Mote Carlo simulatio i order to improve the statistical quality of the results at log imagiary time or low excitatio eergy. Typical imagiary-time correlatio fuctios calculated i quatum Mote Carlo are subject to expoetially growig errors as the rage of imagiary time grows ad this smears the iformatio o the low eergy excitatios. We show that we ca extract the low eergy physics by modifyig the Mote Carlo samplig techique to oe i which cofiguratios which cotribute to makig the histogram of certai quatities flat are promoted. We apply the diagrammatic Mote Carlo (diag-mc) method to the motio of a sigle hole i the t-j model ad we show that the implemetatio of flat-histogram techiques allows us to calculate the Gree s fuctio i a wide rage of imagiary-time. I additio, we show that applyig the flat-histogram techique alleviates the sig -problem associated with the simulatio of the sigle-hole Gree s fuctio at log imagiary time. 1. Itroductio The quatum Mote Carlo simulatio techique has bee very successful whe dealig with equilibrium properties of system of particles which do ot obey Fermi statistics[1]. This techique caot be applied directly to real-time dyamics ad oe resorts i the calculatio of correlatio fuctios i imagiary-time. From the behavior of these correlatio fuctios i imagiary-time, groud-state properties ca be accurately calculated. I priciple, a accurately kow correlatio fuctio at log imagiary-time scales ca yield useful iformatio about the low-lyig eergy excitatios. However, typical imagiary-time correlatio fuctios calculated i quatum Mote Carlo are subject to expoetially growig errors as the rage of imagiary-time grows. Here, we show that this problem ca be sigificatly alleviated by applyig flat-histogram ideas[2, 3, 4] i the Mote Carlo samplig of cofiguratios which cotribute to makig the histogram of such correlatio fuctios i imagiary-time flat. Flat-histogram ideas have bee successfully applied to classical simulatios of systems udergoig first order phase trasitios, systems with rough eergy ladscapes, etc. The Wag-Ladau algorithm[4] (WLA), a particularly useful flat-histogram algorithm, has bee applied to the simulatio of equilibrium statistical mechaical properties of quatum systems[5]. Followig this work, Gull et al.[6] have applied the idea to the cotiuous-time quatum Mote Cotet from this work may be used uder the terms of the Creative Commos Attributio 3. licece. Ay further distributio of this work must maitai attributio to the author(s) ad the title of the work, joural citatio ad DOI. Published uder licece by Ltd 1
3 Carlo approach to the quatum impurity solver eeded for all dyamical mea-field theory applicatios. The mai idea used i the preset paper has bee demostrated[7] i the past usig the Fröhligh polaro problem[8] by combiig the so-called multicaoical[2] (MUCA) or the Wag- Ladau algorithm[4] with the diagrammatic Mote Carlo (diag-mc) method[9, 1]. I the preset paper, we illustrate the beefit of applyig flat-histogram techiques to the diag-mc samplig[9, 1] i order to reveal the imagiary-time depedece of the sigle-hole Gree s fuctio movig i a atiferromagetic backgroud as described by the two-dimesioal (2D) t-j model[11, 12, 13, 14, 15]. The t-j model is probably the simplest o-trivial quatum may-body model to capture the iterplay betwee charge-carrier motio ad atiferromagetic fluctuatios ad it is as basic as the Isig model for classical systems. Therefore, we feel that we should ot proceed ay further with quatum simulatio of electroic systems without havig a techique which ca accurately simulate such a model. First, we use the diag-mc method i cojuctio with the flat-histogram techique to the problem of a sigle-hole i a modified soluble versio of t-j model, where, the diag-mc samplig space is restricted to the diagrams which are summed up by the o-crossig approximatio (NCA). This allows us to assess the correct implemetatio ad accuracy of the method ad to have a exact solutio to compare with the results obtaied with ad without applicatio of the flat-histogram method. Next, we sample all the diagrams without ay restrictio which correspods to the fully liearized versio of the t-j model without ay further approximatio. I this case, the samplig techique should sum up amplitudes which are ot positive defiite ad, thus, they caot be simply iterpreted as probability i the Mote Carlo samplig techique. Namely, we ecouter the so-called sig-problem i the applicatio of the Mote Carlo method. We show that eve i this case, where there is the sig-problem, we ca still extract more accurate results for the imagiary-time depedece of the sigle-hole spectral fuctio ad, thus, more accurate results for the low-lyig spectrum of the problem. We will use two differet implemetatios of the diag-mc method. Oe i which the imagiary-time dimesio is ot sampled by the Markov process ad it is treated as a fixed parameter i a particular simulatio. This requires repetitio of the simulatio for each oe of the differet values of imagiary-time eeded. I the secod implemetatio the imagiarytime is oe additioal dimesio of the samplig space. While the latter approach may be more efficiet, we will also use the former approach to demostrate a differet way to apply the flat-histogram idea i which the distributio of the order cotributio to the perturbatio expasio is made flat. 2. The Hamiltoia We use a simplified versio of the 2D t-j model i which the Hamiltoia ad the hole-hoppig terms are liearized withi the spi-wave approximatio to obtai a polaro-like Hamiltoia ([11, 13, 14]), i.e., Ĥ = k, q g( k, q)a k+ q a k b q + H.c. + k hω(k)b k b k, (1) g( k, q) = 4t N (u q γ k q + υ q γ k ), γ k = 2t(cos(k x a)+cos(k y a)), (2) where b q is the Bogoliubov spi-wave creatio operator, ω(k) is the spi-wave dispersio of the square lattice quatum atiferromaget ad a is the hole creatio operator. Also the g( k, q) k is the couplig of the hole to spi waves ad u q ad υ q are the coefficiets of the Bogoliubov trasformatio. For details of the derivatio of this simplified versio of the t-j model, which 2
4 remais a o-trivial quatum may-body problem, as well as for the defiitios of the operators ad the expressio for ω k, u q, v q ad g( k, q), the reader is referred to Refs. [11, 13, 14]. I order to demostrate the importace of applyig flat-histogram techiques, we will cosider two cases. First, o guidace fuctio is used i the diag-dmc simulatio, which we will refer to as bare diag-mc, which is the straightforward way to apply the diag-mc. This is ot the same as the stadard implemetatio of diag-mc[9, 1] where some differet tricks are applied to assist the simulatio at log imagiary time. The secod approach which will discuss is whe a flat-histogram techique is implemeted i the diag-mc simulatio. Whe we apply ay of the flat-histogram techiques i cojuctio with diag-mc, we will refer to it as flat-histogram diag-mc. 3. Fixed-time diagrammatic Mote Carlo The diagrammatic Mote Carlo techique[9, 1] is a Markov process i a space defied by all the terms (or diagrams) which appear i perturbatio theory. For example, it samples the terms of the perturbatio expasio of the imagiary-time sigle-particle Gree s fuctio, which ca be schematically writte as follows: G = D (λ) = O, O = λ D (λ) (3) d x 1 d x 2...d x S (λ) ( x 1, x 2,..., x,), (4) where O represets the sum of all the diagrams of order, O =G is the Gree s fuctio i zeroth order, ad λ is a variable which labels the cotributio D (λ) of a particular diagram of order. As the order of the expasio icreases, the umber of itegratio variables icreases i a similar maer. Notice that whe we refer to the th order i the case of the t-j model we mea that the umber of spi-wave-propagators cotaied i the diagrams is ; therefore, the order i perturbatio expasio is 2. I diag-mc the radom walk makes a series of trasitios betwee states {, λ, R} {,λ, R },wherer =( x 1,..., x ). Through such a Markov process the etire series of terms is sampled. This process geerates a histogram which represets the umber of times N the order appeared i the Markov process. Sice we ca compute a low order diagram aalytically, say for example the zeroth order O, the absolute value of all other orders is computed as follows: O = N N O. (5) 3.1. The problem with bare applicatio of diag-mc First, we restrict our QMC computatio of G to sample the subspace spaed oly by the diagrams icluded i the o-crossig approximatio (NCA). We do that because i this case, the NCA diagrams ca be summed up exactly ([13, 14]) ad we ca use this exact solutio to judge the accuracy of our results. I Fig. 1 O as a fuctio of is show for a fixed value of as calculated for this soluble model. Notice that the distributio of the order is Gaussia-like which peaks at a value of = max. Fig. 1 shows O o a logarithmic scale for = 8, 12 ad 16. Notice that as a fuctio of, max grows almost liearly with, thevalueofo at the maximum grows dramatically with icreasig. Namely, as icreases higher ad higher order diagrams give the most sigificat cotributio. As a result, for large eough, for ay give limited umber of Mote Carlo steps, the umber of walks ladig i small values of becomes very small or o-existet. However, whe the umber of MC steps which lad i the state = 3
5 6e+11 MUCA NCA AF NCA AF, muca =5, =6, =7 5e+11 MUCA for =12 1e+6 =5 =6 =7 4e+11 O 3e+11 O 2e+11 1e Figure 1. O as a fuctio of for fixed. O as a fuctio of for three differet values of plotted o a logarithmic scale. 2 NCA AF 5e+6 NCA AF 15 bare diag-mc(1) bare diag-mc(2) bare diag-mc(3) 4e+6 bare diag-mc (1) bare diag-mc (2) bare diag-mc (3) O =5 O 3e+6 =7 2e+6 5 1e Figure 2. O as a fuctio of for = 5 as calculated by repeatig the diag-mc for 3 differet startig cofiguratios. Same as part for =7. is zero or very small, it leads to a fatal situatio i our attempt to calculate the absolute value of O, because this is obtaied usig the formula (7) ad a very small N implies a large ucertaity i the absolute value of all O. This is illustrated i Fig. 2 where the calculated O is show as a fuctio of for =5 ad = 7 as calculated by repeatig a diag-mc simulatio of MC steps for 3 differet startig cofiguratios. Notice that the statistical fluctuatios from oe simulatio to aother affect the values of O uiformly for all by the same fluctuatig scale factor, i.e., 1/N. If we are to calculate the error from these fluctuatios for each value of, the size of the error bars would be much larger tha the size of the fluctuatios of the poits for successive values of. Namely, the poits which represet O form a rather smooth curve. This seems uusual 4
6 give the size of the error bars. This ca be explaied by the fact that the error is due to the poor estimatio of N which propagates through the formula give by Eq. 7. Note that usig the same umber of MC steps becomes impossible to calculate O beyod this value of =12 because the ratio O max /O becomes much larger tha the umber of MC steps Applicatio of the flat-histogram techique Here, we will solve the problem discussed i the previous subsectio by adoptig flat-histogram techiques which have bee applied i simulatios of classical statistical mechaics[2, 4]. We map the particular value of to the eergy level i stadard flat-histogram methods for classical statistical mechaics ad the sum of the terms givig O to the desity of states which correspods to the correspodig cofiguratios. The flat-histogram method reormalizes the desity of states O foreach by kow factors (which ca be easily estimated) ad, the, samples a more-or-less flat-histogram of such populatios. 8 MUCA() MUCA(1) MUCA(3) MUCA(6) =2 MUCA() 2.5e+6 2e+6 =7 NCA MUCA MUCA(1) MUCA(2) MUCA(3) =7 N 6 O 1.5e+6, k=(π/2,π/2) 4 1e+6 2 MUCA(6) MUCA(3) 5e+5 MUCA(1) Figure 3. The evolutio of the re-weighted distributio as a fuctio of the multicaoical steps. Notice that the rage where the distributio has a sigificat value expads as we repeat the multicaoical steps ad after 6 such iteratios (gree curve labeled MUCA(6)) it becomes more or less flat i the etire domai. O as a fuctio of for = 7 as calculated by usig multicaoical diag-mc for 3 differet startig cofiguratios ad by applyig the multicaoical method. Next, we use the idea of the MUCA algorithm[2] as follows: First, for a give fixed value of we carry out a iitial exploratory ru, where we fid that the distributio O of the values of peaks at some value of = max, which depeds o the chose value of. The black solid curve i Fig. 3 shows the result obtaied for the histogram usig M =1 6 diag-mc steps. This distributio falls off rapidly for > max, ad, thus, we ca determie the maximum value c of visited by the Markov process. We choose a value of m safely greater tha c, such that the value of O m is practically zero. The, we modify the probabilities associated with a particular cofiguratio of the th order by dividig the origial probabilities by a factor f = max(1,n ). Usig these modified probabilities we carry out aother set of M diag-mc steps which yields a ew histogram with populatios N show by the red curve i Fig. 3. I the ext step, we divide the probabilities associated with a particular cofiguratio of the th order by the factor f = f max(1,n ). Usig these modified probabilities we carry out a ew set of M diag-mc 5
7 steps which yields a ew histogram with populatios N, etc. The blue ad gree curves i Fig. 3 are obtaied i the third ad sixth iteratio of this process. Notice that already at the 6 th step the histogram is reasoably flat. Whe, the histogram becomes more-or-less flat at some k th step, we begi a Markov process for a relatively large umber of MC steps, by dividig the origial probabilities by the factor f (k) factor f (k) we determie O adg., ad, by re-weightig the observables by the biasig Fig. 3 shows the results of applyig the MUCA algorithm as discussed i the previous paragraph for the same umber of MC steps ad approximately the same amout of CPU time as i the calculatio with the straightforward applicatio of the diag-mc to obtai the curves i Fig. 2. Notice the sigificat reductio of the statistical fluctuatios betwee the three differet simulatios. Furthermore, the flat-histogram approach allows us to calculate O for almost ay, somethig which is ot possible usig bare diag-mc. 4. Samplig the imagiary time i the diag-mc Here we discuss the usual implemetatio of the diag-mc where the imagiary time variable is also a dimesio of the samplig space. I this case the radom walk makes a series of trasitios betwee states {, λ, R, i} {,λ, R,i },where R =( x 1,..., x ), is the perturbatio order, λ is a particular diagram, ad i (or i ) is the label of a particular imagiary time iterval ( i δ/2, i + δ/2) (where δ = i+1 i ). Namely, i this case we sample the histogram of G by icludig the imagiary time as a extra dimesio of the samplig space. I this case, the value G i of the histogram G( i )ithei th -iterval is foud by usig the kow value of G i the first iterval, amely, G i = N i N G, (6) where N i is the umber of occurreces of the MC radom walk i the i th iterval. I Fig. 4 we illustrate what the problem is with the bare diag-mc. We first otice that the error i G for a give umber of MC steps grow expoetially with the maximum value of used i this simulatio. The reaso is that because of the expoetial depedece of G o itself (See Fig. 4), give a fixed umber of MC steps, whe is sampled, durig the Markov process, the umber of occurreces N i the = iterval is expoetial small with the value of used. Sice the value of N eters i the Eq. 6 for G i the fluctuatios i G i icrease expoetially with icreasig i this case. This is illustrated i Fig. 4 where the relative errors i G i are give for various values of. For compariso, the relative errors obtaied by usig the Wag-Ladau techique to make the histogram of G i flat ad for approximately the same amout of CPU time to that used i the bare diag-mc simulatio, is also show i Fig. 4. Notice that by icreasig the error i the bare diag-mc icreases more or less uiformly for all values of ad it is much larger tha the error obtaied after the applicatio of the WLA. 5. Results for the full t-j model Here, we preset the results for the full t-j model, where we sample all the diagrams usig diag-mc. First, we will keep the imagiary time fixed ad i the secod part we will preset the case whe is also sampled Fixed-time diagrammatic Mote Carlo First, we ote that some terms i a give order O have a positive cotributio, while some other terms have a egative cotributio. We separate these cotributios to O + ad O such that O = O + O where both O ± are positive. 6
8 bare diag-mc (max=3.8).1 =6. Occurreces/bi 1 Relative Error.1 =5. =3.8 bare diag-mc, =3.8 bare diag-mc, =5. bare diag-mc, =6. diag-mc+wl, = diag-mc+wl bis (time slices) 5 1 Figure 4. G calculated usig bare diag-mc. The relative errors as a fuctio of for various values of whe we use the bare diag-mc method ad whe usig the Wag-Ladau method to make the histogram of G flat. The diag-mc process for fixed discussed i Sec. 3 geerates a histogram which represets the umber of times N +, ad N agiveorder appeared i the Markov process with positive or egative sig. The, we ca compute O ± usig the ratio of these occurreces, i.e., O ± = N ± N + O+. (7) Notice that we have used O + as a referece i both cases because O =. I Fig. 5 we illustrate both cotributios O ± obtaied by applyig the diag-mc+muca samplig for MC steps for = 4 ad = 5. Notice that the two cotributios O + ad O are close ad their relative differece decreases expoetially with, thus, the statistical error icreases expoetially with. This is a maifestatio of the so-called sig-problem i quatum Mote Carlo simulatio. I this case the problem with the bare applicatio of the diag-mc discussed i Sec. 3.1, is sigificatly ehaced due to the icreased fluctuatios i each of the N ± distributios themselves with icreasig. We ca apply the flat-histogram techique as discussed i Sec.3.2 to each of the distributios O ± simultaeously as illustrated i Fig. 6. This approach by reducig the statistical fluctuatios i O ±, for large, allows us to obtai a more accurate G as illustrated i Fig. 6. I Fig. 6 we preset G for the full t-j model usig J/t =.3 ad for k =(π/2,π/2) calculated for 2 differet diag-mc rus usig diag-mc with (red crosses) ad without (ope blue circles) the applicatio of the multicaoical approach ad for the same umber of MC steps i each oe of these MC simulatios. Notice that as icreases the statistical fluctuatios of G obtaied with bare diag-mc are larger tha those obtaied with the applicatio of the flat-histogram techique Samplig the histogram of G. We ca also apply the flat-histogram approach whe we iclude as a dimesio of the samplig space for the full t-j model. I this case, as i Sec. 4, we make the histogram of G flat. Oce agai we wish to demostrate the eed for the applicatio of flat-histogram techiques. I Fig. 7 we preset the positive ad egative cotributios G ± tog calculated with 7
9 6 =4 j=.3, k=(p/2,p/2), t=4 4e+5 =5 j=.3, k=(π/2,π/2), =5 5 positive egative total 3e+5 positive egative total 4 J/t=.3, k=(π/2,π/2) J/t=.3, k=(π/2,π/2) O 3 O 2e Figure 5. The calculated O ± for the full t-j model usig J/t =.3 ad for k =(π/2,π/2) for =4ad =5. =5 j=.3, k=(π/2,π/2), =5 AF FULL, DMC(), MUCA j=.3, k=(π/2,π/2) p-muca() -MUCA() p-muca(1) -MUCA(1) p-muca(2) -MUCA(2) MUCA() J/t=.3, k=(π/2,π/2) N MUCA(2) G 1 1 MUCA(1) bare diag-mc MUCA Figure 6. Demostratio of the applicatio of the flat-histogram approach to make both histograms N ± flat as a fuctio of. This is illustrated for = 5 after oe applicatio of multicaoical step (MUCA(1)) ad after a secod multicaoical step (MUCA(2)). G calculated for 2 diag-mc rus usig diag-mc without (ope blue circles) ad with (red crosses) the applicatio of flat-histogram techique. the bare diag-mc ad i Fig. 7 we preset the results of the calculatio of G adthe G ± cotributios usig diag-mc with the applicatio of the WLA. We ote that while both calculatios were carried out for approximately the same amout of CPU time (2 repetitios of 1 8 MC steps each), for the bare diag-mc case we used =3.8, while for diag-mc+wla we used =4.8. If we were to exted the value of for the former calculatio to the value used i the latter calculatio, most like there would be o occurreces of the Markov 8
10 process i the crucial iterval of =. The reaso is that the expoetial growth of G pushes all the occurreces of the Markov process at high value of. This is evidet for this smaller value of by the large fluctuatios which are more clearly oticeable at low values of. I Fig. 8 we show the relative errors for various values of whe calculated with the bare diag-mc method. Notice that the error i G ± for a give umber of MC steps grows expoetially with the maximum value of used i this simulatio because of the expoetial depedece of G ± o itself. I Fig. 8 we also preset the error obtaied whe the WLA is applied i cojuctio with the diag-mc for approximately the same amout of CPU time. This compariso suggests that the gai of the applicatio of the WLA is sigificat. 1e+6 G + G - G DMC 1e+6 G, Positive Part,Negative Part j=.3,k=(pi/2,pi/2) G + G - G G 1 1 G = = Figure 7. G for the full t-j model (for J/t =.3 ad k =(π/2,π/2)) calculated usig bare diag-mc ad diag-mc+wla. I Fig. 8 the relative errors of G ± adg are show. We ca uderstad the reductio of error whe applyig the flat-histogram techique i a very simple way as follows. For a give value of the error o both G + adg is sigificatly reduced whe combiig the diag- MC techique with the flat-histogram techique. The reaso for this is essetially the same as i the case of the NCA (Sec. 4) where every diagrammatic cotributio is positive defiite. As a result the error i the differece G =G + G is also sigificatly reduced. Thus, the flat-histogram techique is reducig the error i G itself. 6. Discussio We demostrated that the combiatio of the flat-histogram techiques with the diag-mc method yields a sigificat improvemet over the bare diag-mc method. This combiatio has bee applied to extract the imagiary-time sigle-hole Gree s fuctio G ithet-j model. First, we restricted our samplig space to oly those o-crossig diagrams whose cotributio is positive-defiite ad, thus, ca be regarded as a probability distributio. We foud that the combiatio of flat-histogram techiques with the diag-mc method yields much more accurate results for G over a wide rage of the imagiary-time. Secod, whe we sampled the etire diagrammatic space, without the NCA restrictio, there are both positive ad egative cotributios from o NCA diagrams. The positive ad 9
11 .1 DMC Relative Errors j=.3, k=(pi/2,pi/2) =3.8 =4.2.1 G G - G + Relative Error.1.1 diag-mc+wla =3.2 bare diag-mc =3.2 bare diag-mc =3.8 bare diag-mc =4.2 diag-mc+wla Relative Error Figure 8. Relative errors of G obtaied by usig the bare diag-mc for differet values of are compared to the error obtaied by applyig diag-mc+wla. The relative errors to the quatities show i part Fig. 7. egative cotributios approach each other expoetially as we icrease, thus, the error i the differece, i.e., G, grows expoetially with icreasig. This is very similar to the sig problem i other quatum Mote Carlo simulatios of fermios where the statistical error icreases expoetially as a fuctio of the particle umber. The applicatio of the flathistogram techique with the diag-mc allows us to obtai more accurate results o both the positive ad the egative cotributios to G. This eables us to compute G iawider rage of. 7. Ackowledgemets This work was supported i part by the U.S. Natioal High Magetic Field Laboratory, which is fuded by NSF DMR ad the State of Florida. Refereces [1] D. M. Ceperley, Rev. Mod. Phys. 67, 279 (1995). Rev. Mod. Phys. 71, S , (1999). [2] B. A. Berg, ad T. Neuhaus, Phys. Lett. B, 267, 249 (1991). [3] P. M. C. de Oliviera et al., J Phys. 26, 677 (1996). [4] F. Wag ad D. P. Ladau, Phys. Rev. Lett. 86, 25 (21). [5] M. Troyer, S. Wessel, ad F. Alet, Phys. Rev. Lett. 9, 1221 (23). [6] E. Gull, A. J. Millis, A. I. Lichtestei, A. N. Rubstov, M. Troyer, P. Werer, Rev. Mod. Phys. 83, 349 (211). G. Li, W. Werer, ad A. N. Rubstov, S. Base, M. Potthoff, Phys. Rev. B 8, (29). E. Gull, Ph.D. Thesis. [7] N.G.DiamatisadE.Maousakis,Phys.Rev.E,88, 4332 (213). N. G. Diamatis, ad E. Maousakis, Phys. Procedia, 57, 48 (214). [8] H. Fröhlich, H. Pelzer, ad S. Zieau, Philos. Mag. 41, 221 (195). [9] N. V. Prokof ev ad B. V. Svistuov, Phys. Rev. Lett. 81, 2514 (1998). [1] A. S. Mishcheko, N. V. Prokof ev, A. Sakamoto, B. V. Svistuov, Phys. Rev. B, 62, 6317 (2). [11] C. L. Kae, P.A. Lee, ad N. Read, Phys. Rev. B 39, 688 (1989). [12] E. Maousakis, Rev. Mod. Phys. 61, 1 (1991). [13] Z. Liu ad E. Maousakis, Phys. B 44, 2414 (1991). [14] Z. Liu ad E. Maousakis, Phys. B 45, 2425 (1992). [15] E. Dagotto, Rev. Mod. Phys. 66, 763 (1994). 1
Available online at ScienceDirect. Physics Procedia 57 (2014 ) 48 52
Available olie at www.sciecedirect.com ScieceDirect Physics Procedia 57 (214 ) 48 52 27th Aual CSP Workshops o Recet Developmets i Computer Simulatio Studies i Codesed Matter Physics, CSP 214 Applicatio
More informationStatistics 511 Additional Materials
Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability
More informationQuantum Annealing for Heisenberg Spin Chains
LA-UR # - Quatum Aealig for Heiseberg Spi Chais G.P. Berma, V.N. Gorshkov,, ad V.I.Tsifriovich Theoretical Divisio, Los Alamos Natioal Laboratory, Los Alamos, NM Istitute of Physics, Natioal Academy of
More informationRandom Variables, Sampling and Estimation
Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig
More informationA statistical method to determine sample size to estimate characteristic value of soil parameters
A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig
More informationTrue Nature of Potential Energy of a Hydrogen Atom
True Nature of Potetial Eergy of a Hydroge Atom Koshu Suto Key words: Bohr Radius, Potetial Eergy, Rest Mass Eergy, Classical Electro Radius PACS codes: 365Sq, 365-w, 33+p Abstract I cosiderig the potetial
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationDiscrete Mathematics for CS Spring 2008 David Wagner Note 22
CS 70 Discrete Mathematics for CS Sprig 2008 David Wager Note 22 I.I.D. Radom Variables Estimatig the bias of a coi Questio: We wat to estimate the proportio p of Democrats i the US populatio, by takig
More informationDS 100: Principles and Techniques of Data Science Date: April 13, Discussion #10
DS 00: Priciples ad Techiques of Data Sciece Date: April 3, 208 Name: Hypothesis Testig Discussio #0. Defie these terms below as they relate to hypothesis testig. a) Data Geeratio Model: Solutio: A set
More informationKinetics of Complex Reactions
Kietics of Complex Reactios by Flick Colema Departmet of Chemistry Wellesley College Wellesley MA 28 wcolema@wellesley.edu Copyright Flick Colema 996. All rights reserved. You are welcome to use this documet
More informationPolynomial Functions and Their Graphs
Polyomial Fuctios ad Their Graphs I this sectio we begi the study of fuctios defied by polyomial expressios. Polyomial ad ratioal fuctios are the most commo fuctios used to model data, ad are used extesively
More informationWHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? ABSTRACT
WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? Harold G. Loomis Hoolulu, HI ABSTRACT Most coastal locatios have few if ay records of tsuami wave heights obtaied over various time periods. Still
More informationOrthogonal Gaussian Filters for Signal Processing
Orthogoal Gaussia Filters for Sigal Processig Mark Mackezie ad Kiet Tieu Mechaical Egieerig Uiversity of Wollogog.S.W. Australia Abstract A Gaussia filter usig the Hermite orthoormal series of fuctios
More informationNUMERICAL METHODS FOR SOLVING EQUATIONS
Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:
More informationFACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals
More information1 Introduction: within and beyond the normal approximation
Tel Aviv Uiversity, 205 Large ad moderate deviatios Itroductio: withi ad beyod the ormal approximatio a Mathematical prelude................ b Physical prelude................... 3 a Mathematical prelude
More informationSNAP Centre Workshop. Basic Algebraic Manipulation
SNAP Cetre Workshop Basic Algebraic Maipulatio 8 Simplifyig Algebraic Expressios Whe a expressio is writte i the most compact maer possible, it is cosidered to be simplified. Not Simplified: x(x + 4x)
More informationRandom Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices
Radom Matrices with Blocks of Itermediate Scale Strogly Correlated Bad Matrices Jiayi Tog Advisor: Dr. Todd Kemp May 30, 07 Departmet of Mathematics Uiversity of Califoria, Sa Diego Cotets Itroductio Notatio
More informationCHAPTER 10 INFINITE SEQUENCES AND SERIES
CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece
More informationModule 1 Fundamentals in statistics
Normal Distributio Repeated observatios that differ because of experimetal error ofte vary about some cetral value i a roughly symmetrical distributio i which small deviatios occur much more frequetly
More information6.3 Testing Series With Positive Terms
6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationCEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering
CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio
More informationDouble Stage Shrinkage Estimator of Two Parameters. Generalized Exponential Distribution
Iteratioal Mathematical Forum, Vol., 3, o. 3, 3-53 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/.9/imf.3.335 Double Stage Shrikage Estimator of Two Parameters Geeralized Expoetial Distributio Alaa M.
More informationThe time evolution of the state of a quantum system is described by the time-dependent Schrödinger equation (TDSE): ( ) ( ) 2m "2 + V ( r,t) (1.
Adrei Tokmakoff, MIT Departmet of Chemistry, 2/13/2007 1-1 574 TIME-DEPENDENT QUANTUM MECHANICS 1 INTRODUCTION 11 Time-evolutio for time-idepedet Hamiltoias The time evolutio of the state of a quatum system
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationLecture 1 Probability and Statistics
Wikipedia: Lecture 1 Probability ad Statistics Bejami Disraeli, British statesma ad literary figure (1804 1881): There are three kids of lies: lies, damed lies, ad statistics. popularized i US by Mark
More informationThe Random Walk For Dummies
The Radom Walk For Dummies Richard A Mote Abstract We look at the priciples goverig the oe-dimesioal discrete radom walk First we review five basic cocepts of probability theory The we cosider the Beroulli
More informationGUIDELINES ON REPRESENTATIVE SAMPLING
DRUGS WORKING GROUP VALIDATION OF THE GUIDELINES ON REPRESENTATIVE SAMPLING DOCUMENT TYPE : REF. CODE: ISSUE NO: ISSUE DATE: VALIDATION REPORT DWG-SGL-001 002 08 DECEMBER 2012 Ref code: DWG-SGL-001 Issue
More information17 Phonons and conduction electrons in solids (Hiroshi Matsuoka)
7 Phoos ad coductio electros i solids Hiroshi Matsuoa I this chapter we will discuss a miimal microscopic model for phoos i a solid ad a miimal microscopic model for coductio electros i a simple metal.
More informationThere is no straightforward approach for choosing the warmup period l.
B. Maddah INDE 504 Discrete-Evet Simulatio Output Aalysis () Statistical Aalysis for Steady-State Parameters I a otermiatig simulatio, the iterest is i estimatig the log ru steady state measures of performace.
More informationLecture 1 Probability and Statistics
Wikipedia: Lecture 1 Probability ad Statistics Bejami Disraeli, British statesma ad literary figure (1804 1881): There are three kids of lies: lies, damed lies, ad statistics. popularized i US by Mark
More informationNICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) =
AN INTRODUCTION TO SCHRÖDER AND UNKNOWN NUMBERS NICK DUFRESNE Abstract. I this article we will itroduce two types of lattice paths, Schröder paths ad Ukow paths. We will examie differet properties of each,
More informationMATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4
MATH 30: Probability ad Statistics 9. Estimatio ad Testig of Parameters Estimatio ad Testig of Parameters We have bee dealig situatios i which we have full kowledge of the distributio of a radom variable.
More informationBasics of Probability Theory (for Theory of Computation courses)
Basics of Probability Theory (for Theory of Computatio courses) Oded Goldreich Departmet of Computer Sciece Weizma Istitute of Sciece Rehovot, Israel. oded.goldreich@weizma.ac.il November 24, 2008 Preface.
More information4.3 Growth Rates of Solutions to Recurrences
4.3. GROWTH RATES OF SOLUTIONS TO RECURRENCES 81 4.3 Growth Rates of Solutios to Recurreces 4.3.1 Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer.
More information1 Introduction to reducing variance in Monte Carlo simulations
Copyright c 010 by Karl Sigma 1 Itroductio to reducig variace i Mote Carlo simulatios 11 Review of cofidece itervals for estimatig a mea I statistics, we estimate a ukow mea µ = E(X) of a distributio by
More information3/8/2016. Contents in latter part PATTERN RECOGNITION AND MACHINE LEARNING. Dynamical Systems. Dynamical Systems. Linear Dynamical Systems
Cotets i latter part PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 13: SEQUENTIAL DATA Liear Dyamical Systems What is differet from HMM? Kalma filter Its stregth ad limitatio Particle Filter Its simple
More informationComparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series
Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series
More informationInfinite Sequences and Series
Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet
More informationENGI 4421 Confidence Intervals (Two Samples) Page 12-01
ENGI 44 Cofidece Itervals (Two Samples) Page -0 Two Sample Cofidece Iterval for a Differece i Populatio Meas [Navidi sectios 5.4-5.7; Devore chapter 9] From the cetral limit theorem, we kow that, for sufficietly
More informationLesson 10: Limits and Continuity
www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals
More informationAnalysis of Experimental Measurements
Aalysis of Experimetal Measuremets Thik carefully about the process of makig a measuremet. A measuremet is a compariso betwee some ukow physical quatity ad a stadard of that physical quatity. As a example,
More informationTHE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS
R775 Philips Res. Repts 26,414-423, 1971' THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS by H. W. HANNEMAN Abstract Usig the law of propagatio of errors, approximated
More information1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable
More informationAnalytical solutions for multi-wave transfer matrices in layered structures
Joural of Physics: Coferece Series PAPER OPEN ACCESS Aalytical solutios for multi-wave trasfer matrices i layered structures To cite this article: Yu N Belyayev 018 J Phys: Cof Ser 109 01008 View the article
More informationModified Decomposition Method by Adomian and. Rach for Solving Nonlinear Volterra Integro- Differential Equations
Noliear Aalysis ad Differetial Equatios, Vol. 5, 27, o. 4, 57-7 HIKARI Ltd, www.m-hikari.com https://doi.org/.2988/ade.27.62 Modified Decompositio Method by Adomia ad Rach for Solvig Noliear Volterra Itegro-
More informationPower Comparison of Some Goodness-of-fit Tests
Florida Iteratioal Uiversity FIU Digital Commos FIU Electroic Theses ad Dissertatios Uiversity Graduate School 7-6-2016 Power Compariso of Some Goodess-of-fit Tests Tiayi Liu tliu019@fiu.edu DOI: 10.25148/etd.FIDC000750
More informationLecture 2: Monte Carlo Simulation
STAT/Q SCI 43: Itroductio to Resamplig ethods Sprig 27 Istructor: Ye-Chi Che Lecture 2: ote Carlo Simulatio 2 ote Carlo Itegratio Assume we wat to evaluate the followig itegratio: e x3 dx What ca we do?
More informationIMPORTANCE SAMPLING FOR THE SIMULATION OF REINSURANCE LOSSES. Georg Hofmann
Proceedigs of the 213 Witer Simulatio Coferece R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, ad M. E. Kuhl, eds. IMPORTANCE SAMPLING FOR THE SIMULATION OF REINSURANCE LOSSES Georg Validus Research Ic. Suite
More informationSequences. Notation. Convergence of a Sequence
Sequeces A sequece is essetially just a list. Defiitio (Sequece of Real Numbers). A sequece of real umbers is a fuctio Z (, ) R for some real umber. Do t let the descriptio of the domai cofuse you; it
More informationChapter 9: Numerical Differentiation
178 Chapter 9: Numerical Differetiatio Numerical Differetiatio Formulatio of equatios for physical problems ofte ivolve derivatives (rate-of-chage quatities, such as velocity ad acceleratio). Numerical
More informationLecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting
Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would
More informationIP Reference guide for integer programming formulations.
IP Referece guide for iteger programmig formulatios. by James B. Orli for 15.053 ad 15.058 This documet is iteded as a compact (or relatively compact) guide to the formulatio of iteger programs. For more
More informationSimilarity between quantum mechanics and thermodynamics: Entropy, temperature, and Carnot cycle
Similarity betwee quatum mechaics ad thermodyamics: Etropy, temperature, ad Carot cycle Sumiyoshi Abe 1,,3 ad Shiji Okuyama 1 1 Departmet of Physical Egieerig, Mie Uiversity, Mie 514-8507, Japa Istitut
More information6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer.
6 Itegers Modulo I Example 2.3(e), we have defied the cogruece of two itegers a,b with respect to a modulus. Let us recall that a b (mod ) meas a b. We have proved that cogruece is a equivalece relatio
More informationMAT 271 Project: Partial Fractions for certain rational functions
MAT 7 Project: Partial Fractios for certai ratioal fuctios Prerequisite kowledge: partial fractios from MAT 7, a very good commad of factorig ad complex umbers from Precalculus. To complete this project,
More informationTHE KALMAN FILTER RAUL ROJAS
THE KALMAN FILTER RAUL ROJAS Abstract. This paper provides a getle itroductio to the Kalma filter, a umerical method that ca be used for sesor fusio or for calculatio of trajectories. First, we cosider
More informationStat 421-SP2012 Interval Estimation Section
Stat 41-SP01 Iterval Estimatio Sectio 11.1-11. We ow uderstad (Chapter 10) how to fid poit estimators of a ukow parameter. o However, a poit estimate does ot provide ay iformatio about the ucertaity (possible
More informationProbability, Expectation Value and Uncertainty
Chapter 1 Probability, Expectatio Value ad Ucertaity We have see that the physically observable properties of a quatum system are represeted by Hermitea operators (also referred to as observables ) such
More informationTIME-CORRELATION FUNCTIONS
p. 8 TIME-CORRELATION FUNCTIONS Time-correlatio fuctios are a effective way of represetig the dyamics of a system. They provide a statistical descriptio of the time-evolutio of a variable for a esemble
More information62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +
62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of
More informationOn an Application of Bayesian Estimation
O a Applicatio of ayesia Estimatio KIYOHARU TANAKA School of Sciece ad Egieerig, Kiki Uiversity, Kowakae, Higashi-Osaka, JAPAN Email: ktaaka@ifokidaiacjp EVGENIY GRECHNIKOV Departmet of Mathematics, auma
More informationChapter 2 The Monte Carlo Method
Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful
More informationSequences of Definite Integrals, Factorials and Double Factorials
47 6 Joural of Iteger Sequeces, Vol. 8 (5), Article 5.4.6 Sequeces of Defiite Itegrals, Factorials ad Double Factorials Thierry Daa-Picard Departmet of Applied Mathematics Jerusalem College of Techology
More informationLecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting
Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would
More informationConfidence Interval for Standard Deviation of Normal Distribution with Known Coefficients of Variation
Cofidece Iterval for tadard Deviatio of Normal Distributio with Kow Coefficiets of Variatio uparat Niwitpog Departmet of Applied tatistics, Faculty of Applied ciece Kig Mogkut s Uiversity of Techology
More informationCSE 527, Additional notes on MLE & EM
CSE 57 Lecture Notes: MLE & EM CSE 57, Additioal otes o MLE & EM Based o earlier otes by C. Grat & M. Narasimha Itroductio Last lecture we bega a examiatio of model based clusterig. This lecture will be
More informationThe Riemann Zeta Function
Physics 6A Witer 6 The Riema Zeta Fuctio I this ote, I will sketch some of the mai properties of the Riema zeta fuctio, ζ(x). For x >, we defie ζ(x) =, x >. () x = For x, this sum diverges. However, we
More informationResampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.
Jauary 1, 2019 Resamplig Methods Motivatio We have so may estimators with the property θ θ d N 0, σ 2 We ca also write θ a N θ, σ 2 /, where a meas approximately distributed as Oce we have a cosistet estimator
More informationSimilarity Solutions to Unsteady Pseudoplastic. Flow Near a Moving Wall
Iteratioal Mathematical Forum, Vol. 9, 04, o. 3, 465-475 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/0.988/imf.04.48 Similarity Solutios to Usteady Pseudoplastic Flow Near a Movig Wall W. Robi Egieerig
More informationBasis for simulation techniques
Basis for simulatio techiques M. Veeraraghava, March 7, 004 Estimatio is based o a collectio of experimetal outcomes, x, x,, x, where each experimetal outcome is a value of a radom variable. x i. Defiitios
More information10. Comparative Tests among Spatial Regression Models. Here we revisit the example in Section 8.1 of estimating the mean of a normal random
Part III. Areal Data Aalysis 0. Comparative Tests amog Spatial Regressio Models While the otio of relative likelihood values for differet models is somewhat difficult to iterpret directly (as metioed above),
More informationNotes on iteration and Newton s method. Iteration
Notes o iteratio ad Newto s method Iteratio Iteratio meas doig somethig over ad over. I our cotet, a iteratio is a sequece of umbers, vectors, fuctios, etc. geerated by a iteratio rule of the type 1 f
More informationFrequency Domain Filtering
Frequecy Domai Filterig Raga Rodrigo October 19, 2010 Outlie Cotets 1 Itroductio 1 2 Fourier Represetatio of Fiite-Duratio Sequeces: The Discrete Fourier Trasform 1 3 The 2-D Discrete Fourier Trasform
More informationLecture 25 (Dec. 6, 2017)
Lecture 5 8.31 Quatum Theory I, Fall 017 106 Lecture 5 (Dec. 6, 017) 5.1 Degeerate Perturbatio Theory Previously, whe discussig perturbatio theory, we restricted ourselves to the case where the uperturbed
More informationECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015
ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],
More information1 Adiabatic and diabatic representations
1 Adiabatic ad diabatic represetatios 1.1 Bor-Oppeheimer approximatio The time-idepedet Schrödiger equatio for both electroic ad uclear degrees of freedom is Ĥ Ψ(r, R) = E Ψ(r, R), (1) where the full molecular
More information(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:
Math 5-4 Tue Feb 4 Cotiue with sectio 36 Determiats The effective way to compute determiats for larger-sized matrices without lots of zeroes is to ot use the defiitio, but rather to use the followig facts,
More informationThe standard deviation of the mean
Physics 6C Fall 20 The stadard deviatio of the mea These otes provide some clarificatio o the distictio betwee the stadard deviatio ad the stadard deviatio of the mea.. The sample mea ad variace Cosider
More informationApproximate Confidence Interval for the Reciprocal of a Normal Mean with a Known Coefficient of Variation
Metodološki zvezki, Vol. 13, No., 016, 117-130 Approximate Cofidece Iterval for the Reciprocal of a Normal Mea with a Kow Coefficiet of Variatio Wararit Paichkitkosolkul 1 Abstract A approximate cofidece
More informationGoodness-Of-Fit For The Generalized Exponential Distribution. Abstract
Goodess-Of-Fit For The Geeralized Expoetial Distributio By Amal S. Hassa stitute of Statistical Studies & Research Cairo Uiversity Abstract Recetly a ew distributio called geeralized expoetial or expoetiated
More informationDEGENERACY AND ALL THAT
DEGENERACY AND ALL THAT Te Nature of Termodyamics, Statistical Mecaics ad Classical Mecaics Termodyamics Te study of te equilibrium bulk properties of matter witi te cotext of four laws or facts of experiece
More informationMONTE CARLO VARIANCE REDUCTION METHODS
MONTE CARLO VARIANCE REDUCTION METHODS M. Ragheb /4/3. INTRODUCTION The questio arises of whether oe ca reduce the variace associated with the samplig of a radom variable? Ideed we ca, but we eed to be
More informationActivity 3: Length Measurements with the Four-Sided Meter Stick
Activity 3: Legth Measuremets with the Four-Sided Meter Stick OBJECTIVE: The purpose of this experimet is to study errors ad the propagatio of errors whe experimetal data derived usig a four-sided meter
More information10-701/ Machine Learning Mid-term Exam Solution
0-70/5-78 Machie Learig Mid-term Exam Solutio Your Name: Your Adrew ID: True or False (Give oe setece explaatio) (20%). (F) For a cotiuous radom variable x ad its probability distributio fuctio p(x), it
More informationChapter 6 Part 5. Confidence Intervals t distribution chi square distribution. October 23, 2008
Chapter 6 Part 5 Cofidece Itervals t distributio chi square distributio October 23, 2008 The will be o help sessio o Moday, October 27. Goal: To clearly uderstad the lik betwee probability ad cofidece
More informationMicroscopic Theory of Transport (Fall 2003) Lecture 6 (9/19/03) Static and Short Time Properties of Time Correlation Functions
.03 Microscopic Theory of Trasport (Fall 003) Lecture 6 (9/9/03) Static ad Short Time Properties of Time Correlatio Fuctios Refereces -- Boo ad Yip, Chap There are a umber of properties of time correlatio
More informationw (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ.
2 5. Weighted umber of late jobs 5.1. Release dates ad due dates: maximimizig the weight of o-time jobs Oce we add release dates, miimizig the umber of late jobs becomes a sigificatly harder problem. For
More informationMath 10A final exam, December 16, 2016
Please put away all books, calculators, cell phoes ad other devices. You may cosult a sigle two-sided sheet of otes. Please write carefully ad clearly, USING WORDS (ot just symbols). Remember that the
More informationMath 312 Lecture Notes One Dimensional Maps
Math 312 Lecture Notes Oe Dimesioal Maps Warre Weckesser Departmet of Mathematics Colgate Uiversity 21-23 February 25 A Example We begi with the simplest model of populatio growth. Suppose, for example,
More information6. Sufficient, Complete, and Ancillary Statistics
Sufficiet, Complete ad Acillary Statistics http://www.math.uah.edu/stat/poit/sufficiet.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 6. Sufficiet, Complete, ad Acillary
More informationThe Sample Variance Formula: A Detailed Study of an Old Controversy
The Sample Variace Formula: A Detailed Study of a Old Cotroversy Ky M. Vu PhD. AuLac Techologies Ic. c 00 Email: kymvu@aulactechologies.com Abstract The two biased ad ubiased formulae for the sample variace
More informationMixtures of Gaussians and the EM Algorithm
Mixtures of Gaussias ad the EM Algorithm CSE 6363 Machie Learig Vassilis Athitsos Computer Sciece ad Egieerig Departmet Uiversity of Texas at Arligto 1 Gaussias A popular way to estimate probability desity
More informationProvläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE
TECHNICAL REPORT CISPR 16-4-3 2004 AMENDMENT 1 2006-10 INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE Amedmet 1 Specificatio for radio disturbace ad immuity measurig apparatus ad methods Part 4-3:
More informationQuantum Simulation: Solving Schrödinger Equation on a Quantum Computer
Purdue Uiversity Purdue e-pubs Birc Poster Sessios Birc Naotechology Ceter 4-14-008 Quatum Simulatio: Solvig Schrödiger Equatio o a Quatum Computer Hefeg Wag Purdue Uiversity, wag10@purdue.edu Sabre Kais
More informationDiscrete Orthogonal Moment Features Using Chebyshev Polynomials
Discrete Orthogoal Momet Features Usig Chebyshev Polyomials R. Mukuda, 1 S.H.Og ad P.A. Lee 3 1 Faculty of Iformatio Sciece ad Techology, Multimedia Uiversity 75450 Malacca, Malaysia. Istitute of Mathematical
More informationc. Explain the basic Newsvendor model. Why is it useful for SC models? e. What additional research do you believe will be helpful in this area?
1. Research Methodology a. What is meat by the supply chai (SC) coordiatio problem ad does it apply to all types of SC s? Does the Bullwhip effect relate to all types of SC s? Also does it relate to SC
More informationChapter 6 Principles of Data Reduction
Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a
More informationANALYSIS OF EXPERIMENTAL ERRORS
ANALYSIS OF EXPERIMENTAL ERRORS All physical measuremets ecoutered i the verificatio of physics theories ad cocepts are subject to ucertaities that deped o the measurig istrumets used ad the coditios uder
More information