Optimal Paired Choice Block Designs. Supplementary Material

Size: px
Start display at page:

Download "Optimal Paired Choice Block Designs. Supplementary Material"

Transcription

1 Saisica Sinica: Supplemen Opimal Paired Choice Block Designs Rakhi Singh 1, Ashish Das 2 and Feng-Shun Chai 3 1 IITB-Monash Research Academy, Mumbai, India 2 Indian Insiue of Technology Bombay, Mumbai, India 3 Academia Sinica, Taipei, Taiwan Supplemenary Maerial Design for Example 3.1. k = 3, v 1 = 2, v 2 = 3, v 3 = 4, b = 1, N = s = 72. (000,111) (000,222) (000,333) (100,211) (100,322) (100,033) (020,131) (020,313) (120,302) (001,112) (001,223) (001,330) (101,212) (101,323) (101,030) (021,132) (021,310) (121,303) (002,113) (002,220) (002,331) (102,213) (102,320) (102,031) (022,133) (022,311) (122,300) (003,110) (003,221) (003,332) (103,210) (103,321) (103,032) (023,130) (023,312) (123,301) (010,121) (010,232) (010,303) (110,221) (110,332) (110,003) (020,202) (120,231) (120,013) (011,122) (011,233) (011,300) (111,222) (111,333) (111,000) (021,203) (121,232) (121,010) (012,123) (012,230) (012,301) (112,223) (112,330) (112,001) (022,200) (122,233) (122,011) (013,120) (013,231) (013,302) (113,220) (113,331) (113,002) (023,201) (123,230) (123,012) Lemma 1. A necessary and sufficien condiion for C M = C M o hold is ha for each block and each aribue, he frequency disribuion of he levels of he aribue are same for he wo opions. Proof. Le P Mj = ((P j) 1 (P j) (P j) b) where (P j) represens P Mj for he h block. Then

2 Rakhi Singh, Ashish Das and Feng-Shun Chai he condiion W P M = 0 is equivalen o he condiion 1 (P 1) = 1 (P 2), = 1,..., b. Le (P j) = ((P j) 1 (P j) w (P j) k ) where (P j) w is of order s (v w 1) and represens (P j) for he wh aribue. Therefore for = 1,..., b, if 1 (P 1) = 1 (P 2), hen 1 (P 1) w = 1 (P 2) w for every w and. Now, since he ih column of (P j) w provides frequency of level i and level v w in he wh aribue of he jh opion in he h block, herefore, 1 (P 1) w = 1 (P 2) w implies ha he frequency of each of he levels of aribue w is same in he wo opions among he s choice pairs in block. The converse follows by noing ha if for each block and each aribue, he frequency disribuion of he levels of he aribue are same for he wo opions, hen 1 (P 1) = 1 (P 2) for every. Proof of Theorem 1. The proof follows as a special case of Lemma 1. Proof of Theorem 2. Under he linear paired comparison model, a design d opimally esimaes he main effecs if C M = diag(c (1),..., C (k) ) (see Großmann and Schwabe (2015)) where C (i) = z i(i vi 1 + J vi 1) wih z i = 2N/(v i 1), i = 1,..., k. This implies ha C M normalized by number of pairs would aain an opimal srucure if C (i) = z i(i vi 1 + J vi 1) wih z i = 2/(v i 1), i = 1,..., k. Since he OA + G mehod of consrucion enails adding generaors o he orhogonal array of srengh, ( 2), he off-diagonal elemens of P M P M corresponding o wo differen aribues is zero since under each level of he firs aribue, all he levels of he second aribue occur equally ofen. Also, since in an orhogonal array, under each column (aribue) he levels are equally replicaed, o esablish ha each C (i) aains an opimal srucure of he form z i(i vi 1 + J vi 1), i is enough o show ha normalized P M P M corresponding o a paired choice design wih one aribue, say a v levels, aains he srucure z(i v 1 + J v 1), where z = 2/(v 1).

3 Opimal Paired Choice Block Designs Wihou loss of generaliy, we consider only v choice pairs for a ypical aribue since under each column, he n rows of he orhogonal array involves v symbols each replicaed n/v imes. While using he generaor g j, le P 0 1, P j 2 be he v (v 1) effecs-coded marix for he main effecs for he firs and second opions, respecively, corresponding o any one aribue a v levels. When h > 1, noe ha P M is he collecion of differen marices generaed ou of he corresponding {P 0 1, P j 2 }, j = 1..., h of choice pairs. For noaional simpliciy, we denoe P 0 1 by P 0 and P j 2 by Pj, j = 1,..., v 1. Also, noe ha 1 P j = 0 and v 1 j=0 Pj = 0. Consider he informaion marix P M P M normalized for v even. v(v 1)P M P M = v 1 (P0 P j) (P 0 P j) = v 1 (P 0P 0 + P jp j P 0P j P jp 0) = v 1 {2(Iv 1 + Jv 1)} P 0( v 1 Pj) ( v 1 P j)p 0 = {2(v 1)(I v 1+J v 1)} P 0( P 0) ( P 0)P 0 = 2{(v 1)(I v 1+J v 1)}+2P 0P 0 = 2v(I v 1 + J v 1). Thus, for v even, h = v 1 generaors of he ype g j = 1,..., v 1 leads o he opimal srucure of normalized P M P M. For v odd, we noe ha, if say, mh row of P 0 corresponds o he level i, hen he mh row of P v j corresponds o he level i j (mod v). Similarly, if say, lh row of P j corresponds o he level i, hen he lh row of P 0 corresponds o he level i j (mod v). This makes he lh row of P j and P 0 same as he mh row of P 0 and P v j for every wo rows l m = 1,..., v. Therefore, for v odd, P jp 0 = P 0P v j. Now, v(v 1)/2P M P M = (v 1)/2 (P 0 P j) (P 0 P j) = (v 1)/2 (P 0P 0 + P jp j P 0P j P jp 0) = (v 1)/2 {2(I v 1 + J v 1)} (v 1)/2 (P 0P j + P jp 0) = (v 1)(I (v 1)/2 v 1+J v 1) (P 0P j+p 0P v j) = (v 1)(I v 1+J v 1) P 0 (v 1)/2 (P j+p v j) = (v 1)(I v 1 +J v 1) P 0 v 1 Pj = (v 1)(Iv 1 +Jv 1) P 0( P 0) = v(i v 1 +J v 1). Thus, for v odd, h = (v 1)/2 generaors of he ype g j = 1,..., (v 1)/2 leads o he opimal srucure of normalized P M P M. Proof of Theorem 3. For a given OA(n 1, k + 1, v 1 v k δ, 2), corresponding o he k aribues a levels v i, i = 1,..., k, le d 1 be he design consruced hrough OA + G mehod

4 Rakhi Singh, Ashish Das and Feng-Shun Chai using h = lcm(v 1,..., v k ) generaors. Then d 1 wih parameers k, v 1,..., v k, b = 1, s = hn 1 is an opimal paired choice design. From d 1, he choice pairs obained hrough each of he h generaors consiue a block of size n 1. This is rue since n 1 rows of a block form he orhogonal array in he firs opion and, wih labels re-coded hrough he generaor, in he second opion and hence he condiions in Theorem 1 are saisfied. Finally, we use he δ symbols of he (k + 1)h column of he orhogonal array for furher blocking giving a paired choice block design d 2 wih parameers k, v 1,..., v k, b = hδ, s = n 1/δ. This is rue since for every aribue in each of he blocks so formed, each of he v i levels occurs equally ofen under ih aribue and hence by Theorem 1, d 2 is opimal in D k,b,s. Proofs for Theorem 4 and Theorem 5 require a resul from Dey (2009) ha is given below. Lemma 2 (Dey (2009)). Consider v(v 1)/2 combinaions involving v levels aken wo a a ime. Then, for v odd, he combinaions can be grouped ino (v 1)/2 replicaes each comprising v combinaions. The groups are {(i, v 2 i), (i+1, v 1 i),..., (i+v 1, v 2 (i (v 1)))} and he levels are reduced modulo v; i = 0,..., (v 3)/2. Proof of Theorem 4. Theorem 3 of Graßhoff e al. (2004) saes ha from m( k) rows of a Hadamard marix H m of order m, an opimal paired choice design d 3 wih parameers k, v, b = 1, s = mv(v 1)/2 is consruced using he v(v 1)/2 combinaions of v levels aken wo a a ime. From every row of {H m, H m}, v(v 1)/2 choice pairs are obained by replacing 1 in he row by he firs column of he combinaions and 1 in he row by he second column of he combinaions. If v is odd, hen (v 1)/2 is an ineger and he v(v 1)/2 combinaions can be arranged in rows such ha each of he wo columns have every level appearing equally ofen. Such an arrangemen is always possible and follows from sysems of disinc represenaives. Therefore, corresponding o each of he rows of {H m, H m}, using v(v 1)/2 choice pairs as a block, a paired choice block design wih parameers k, v, b = m, s = v(v 1)/2 is obained

5 Opimal Paired Choice Block Designs which, following Theorem 1 is opimal. Now for v odd, from Dey (2009), v(v 1)/2 combinaions involving v levels aken wo a a ime can be grouped ino (v 1)/2 replicaes each comprising v combinaions. Therefore, he blocks generaed by each row of H m can be furher broken ino (v 1)/2 blocks each of size v, which gives us d 4. Proof of Theorem 5. Consrucion 3.2 of Demirkale, Donovan, and Sree (2013) uses an OA(n 2, k + 1, v k v k+1, 2) wih v k+1 = n 2/v and forms v k+1 parallel ses each having v rows. ( Then, an opimal paired choice design wih parameers k, v, b = 1, s = v v k+1 2) is consruced using he v(v 1)/2 combinaions of v numbers {1,..., v} aken wo a a ime. Le {i, j} be a ypical row. Then, for each such row of size wo, corresponding rows i and j from each of he v k+1 parallel ses are chosen o form he choice pairs of he opimal paired choice design d 6. Again as earlier, for v odd, he v(v 1)/2 combinaions can be arranged in rows such ha each of he wo columns have every number appearing equally ofen. Considering he v(v 1)/2 choice pairs, obained from a parallel se, as a block, we ge he paired choice block design wih parameers k, v, b = v k+1, s = v(v 1)/2 which is opimal in D k,b,s. Furher proof follows on he same lines as he proof of Theorem 4 by reaing he pairs generaed by each parallel se as blocks. Proof of Theorem 6. Theorem 4 of Graßhoff e al. (2004) uses an OA(n 3, k + 1, m 1 m k δ, 2) wih m i = v i(v i 1)/2 for some odd v i o consruc an opimal paired choice design d 7 wih parameers k, v i,..., v k, b = 1, s = n 3. This mehod involves a one-one mapping beween m i levels of orhogonal array o he v i(v i 1)/2 combinaions on v i symbols. For a combinaion {i, j} corresponding o a symbol of an orhogonal array, he firs opion in a pair is obained by replacing i in place of ha symbol and he second opion has j in he corresponding posiion. Then, similar o consrucion of Theorem 3, using he δ ( 1) symbols of he (k + 1)h column of he orhogonal array for blocking gives us an opimal paired choice block design d 8 wih

6 Rakhi Singh, Ashish Das and Feng-Shun Chai parameers k, v i,..., v k, b = δ, s = n 3/δ. Noe ha his mehod is applicable only for odd v i since for even v i, i is no possible o arrange v i(v i 1)/2 combinaions in a posiion-balanced manner. Proof of Theorem 7. From Theorem 1, for each of he h generaors, a paired choice design using he OA+G mehod of consrucion is opimal under he broader main effecs block model if P M P I = 0. For a given generaor, o show ha P M P I = 0, i suffices o show ha he inner produc of he columns of P M corresponding o he mh main effec and he columns of P I corresponding o he wo-facor ineracion effec of ih and jh aribue is zero. Using an OA(n 1, k, v 1 v k, 3) in he OA + G mehod of consrucion, we esablish he resul hrough he following wo cases. Case (i) m = i: In an orhogonal array of srengh 2, each of he v iv j combinaions occur equally ofen n 1/(v iv j) imes as rows. Therefore, since he paired choice design is based on he orhogonal array, for showing ha P M P I = 0, i suffices o show ha P M P I = 0 for one of he n 1/(v iv j) ses of v iv j rows of he ype (i, j); i = 0,..., v i 1; j = 0,..., v j 1. For such v iv j rows, noe ha P My, (y = 1, 2), corresponding o he jh aribue, can be pariioned ino v i ses P My(j) each of v j disinc rows. Then, 1 P My(j) = 0. Le P Iy corresponding o he ih aribue fixed a level i l (i l = 0,..., v i 1) and he jh aribue aking v j disinc levels be represened by P Iy(il j). Then, he columns of P Iy(il j) are muliples of eiher P My(j) or 0 v. Therefore, 1 P Iy(il j) = 0 for y = 1, 2. Le P M corresponding o he ih aribue a level i l be represened by X il. Then, X il = 1x i l where x i l is a row vecor of size v i 1. Therefore, P M P I = v i 1 i l =0 X i l (P I1(il j) P I2(il j)) = vi 1 i l =0 xi l (1 P I1(il j) 1 P I2(il j)) = 0. Case (ii) m i: In an orhogonal array of srengh 3, each of he v iv jv m combinaions

7 Opimal Paired Choice Block Designs occur equally ofen n 1/(v mv iv j) imes as rows. Therefore, as in Case (i), for showing ha P M P I = 0, i suffices o show ha P M P I = 0 for one of he n 1/(v mv iv j) ses of v mv iv j rows of he ype (m, i, j); m = 0,..., v m 1; i = 0,..., v i 1; j = 0,..., v j 1. For such v mv iv j rows, noe ha P Iy, (y = 1, 2), corresponding o he ih and jh aribue, can be pariioned ino v m ses P Iy(ij) each of v iv j disinc rows. Therefore, 1 P Iy(ij) = 0 for y = 1, 2, since from Case (i), 1 P Iy(il j) = 0 for he ih aribue a level i l. Finally, since for he mh aribue a level m l (m l = 0,..., v m 1), he v iv j combinaions under aribues i and j occur equally ofen, herefore P M P I = v m 1 m l =0 X m l (P I1(ij) P I2(ij) ) = vm 1 m l =0 xm l (1 P I1(ij) 1 P I2(ij) ) = 0. Proof of Theorem 10. From Lemma 1, W P M = 0 if and only if for each aribue under he choice pairs having foldover in he second opion of a choice pair, he level l (l = 0, 1) appears equally ofen in boh he opions in every block and hus, he frequency of he pair (1, 0) is same as he frequency of he pair (0, 1) under every aribue in each block. Le P I = (Y 1 Y Y b ) where Y is he s k(k 1)/2 marix corresponding o he h block. Wih (P Ij) represening P Ij for he h block, Y = (P I1) (P I2). Then, he condiion W P I = 0 is equivalen o he condiion 1 (P I1) = 1 (P I2) for every = 1,..., b. Consider (P Ij) = ((P Ij) 12 (P Ij) lm (P Ij) (k 1)k ) where (P Ij) lm is of order s 1 and represens (P Ij) for he wo-facor ineracion beween he lh and he mh aribue. Therefore, he necessary and sufficien condiion for 1 (P I1) = 1 (P I2) is ha 1 (P I1) lm = 1 (P I2) lm for every l and m. In he h block, for he choice pairs where eiher boh he aribues have a foldover in he second opion or boh do no have a foldover in he second opion, he corresponding rows in (P I2) lm are same as he corresponding rows in (P I1) lm. However, for he pairs in which one aribue has a foldover in he second opion and anoher does no have foldover in he second opion, he corresponding rows in (P I2) lm are

8 Rakhi Singh, Ashish Das and Feng-Shun Chai negaive of he corresponding rows in (P I1) lm. In such a case, 1 (P I1) lm = 1 (P I2) lm if and only if 1 (P I1) lm = 1 (P I2) lm = 0. Now, 1 (P I1) lm = 0 if and only if he frequency of he pairs from he se {(01, 00), (01, 11), (10, 00),(10, 11)} is same as he frequency of he pairs from he se {(00, 01), (00, 10), (11, 01), (11, 10)} under he lh and he mh aribue. Proof of Theorem 11. In seps (iii)-(iv), corresponding o an elemen f of F, make he firs se of 2 α 1 2 k α 2 = 2 k 3 blocks having choice pairs (ab, a b), (ab, a b ), (a c, ac), (a c, ac ). Similarly, following he seps (iii)-(iv), we make an addiional se of 2 k 3 blocks having choice pairs (ac, a c), (ac, a c ), (a b, ab), (a b, ab ). Noe ha each of he consruced blocks saisfy condiions (i) and (ii) of Theorem 10. This gives rise o a oal of 2 k 2 ses of blocks each of size 4. The way we have consruced he choice pairs in seps (iii)-(iv), i follows ha he collecion of firs opion in he 2 k choice pairs forms a complee facorial having 2 k combinaions. Furhermore, he addiional se of 2 k 3 blocks, in he consrucion, is idenical o he firs se of 2 k 3 blocks. Accordingly, we reain only he firs se of 2 k 3 blocks. This gives rise o a oal of 2 k 1 choice pairs divided ino 2 k 3 blocks each of size 4. Therefore, sep (v) gives an opimal paired choice block design d I 2 wih parameers k, v = 2, s = 4, b where b = 2 k 3( k q) for k odd and b = 2 k 3( k+1 q+1) for k even. References Demirkale, F., D. Donovan, and D. J. Sree (2013). Consrucing D-opimal symmeric saed preference discree choice experimens. J. Sais. Plann. Inference 143 (8), Dey, A. (2009). Orhogonally blocked hree-level second order designs. J. Sais. Plann. Inference 139 (10),

9 REFERENCES Graßhoff, U., H. Großmann, H. Holling, and R. Schwabe (2004). Opimal designs for main effecs in linear paired comparison models. J. Sais. Plann. Inference 126 (1), Großmann, H. and R. Schwabe (2015). Design for discree choice experimens. In A. Dean, M. Morris, J. Sufken, and D. Bingham (Eds.), Handbook of Design and Analysis of Experimens, pp Boca Raon, FL: Chapman and Hall. Rakhi Singh, IITB-Monash Research Academy, Mumbai, India Ashish Das, Indian Insiue of Technology Bombay, Mumbai, India Feng-Shun Chai, Academia Sinica, Taipei, Taiwan

0.1 MAXIMUM LIKELIHOOD ESTIMATION EXPLAINED

0.1 MAXIMUM LIKELIHOOD ESTIMATION EXPLAINED 0.1 MAXIMUM LIKELIHOOD ESTIMATIO EXPLAIED Maximum likelihood esimaion is a bes-fi saisical mehod for he esimaion of he values of he parameers of a sysem, based on a se of observaions of a random variable

More information

STATE-SPACE MODELLING. A mass balance across the tank gives:

STATE-SPACE MODELLING. A mass balance across the tank gives: B. Lennox and N.F. Thornhill, 9, Sae Space Modelling, IChemE Process Managemen and Conrol Subjec Group Newsleer STE-SPACE MODELLING Inroducion: Over he pas decade or so here has been an ever increasing

More information

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB Elecronic Companion EC.1. Proofs of Technical Lemmas and Theorems LEMMA 1. Le C(RB) be he oal cos incurred by he RB policy. Then we have, T L E[C(RB)] 3 E[Z RB ]. (EC.1) Proof of Lemma 1. Using he marginal

More information

Exercises: Similarity Transformation

Exercises: Similarity Transformation Exercises: Similariy Transformaion Problem. Diagonalize he following marix: A [ 2 4 Soluion. Marix A has wo eigenvalues λ 3 and λ 2 2. Since (i) A is a 2 2 marix and (ii) i has 2 disinc eigenvalues, we

More information

Electrical and current self-induction

Electrical and current self-induction Elecrical and curren self-inducion F. F. Mende hp://fmnauka.narod.ru/works.hml mende_fedor@mail.ru Absrac The aricle considers he self-inducance of reacive elemens. Elecrical self-inducion To he laws of

More information

Stat 601 The Design of Experiments

Stat 601 The Design of Experiments Sa 601 The Design of Experimens Yuqing Xu Deparmen of Saisics Universiy of Wisconsin Madison, WI 53706, USA December 1, 2016 Yuqing Xu (UW-Madison) Sa 601 Week 12 December 1, 2016 1 / 17 Lain Squares Definiion

More information

Notes for Lecture 17-18

Notes for Lecture 17-18 U.C. Berkeley CS278: Compuaional Complexiy Handou N7-8 Professor Luca Trevisan April 3-8, 2008 Noes for Lecure 7-8 In hese wo lecures we prove he firs half of he PCP Theorem, he Amplificaion Lemma, up

More information

10. State Space Methods

10. State Space Methods . Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he

More information

Expert Advice for Amateurs

Expert Advice for Amateurs Exper Advice for Amaeurs Ernes K. Lai Online Appendix - Exisence of Equilibria The analysis in his secion is performed under more general payoff funcions. Wihou aking an explici form, he payoffs of he

More information

SUFFICIENT CONDITIONS FOR EXISTENCE SOLUTION OF LINEAR TWO-POINT BOUNDARY PROBLEM IN MINIMIZATION OF QUADRATIC FUNCTIONAL

SUFFICIENT CONDITIONS FOR EXISTENCE SOLUTION OF LINEAR TWO-POINT BOUNDARY PROBLEM IN MINIMIZATION OF QUADRATIC FUNCTIONAL HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, Series A, OF HE ROMANIAN ACADEMY Volume, Number 4/200, pp 287 293 SUFFICIEN CONDIIONS FOR EXISENCE SOLUION OF LINEAR WO-POIN BOUNDARY PROBLEM IN

More information

Model Reduction for Dynamical Systems Lecture 6

Model Reduction for Dynamical Systems Lecture 6 Oo-von-Guericke Universiä Magdeburg Faculy of Mahemaics Summer erm 07 Model Reducion for Dynamical Sysems ecure 6 v eer enner and ihong Feng Max lanck Insiue for Dynamics of Complex echnical Sysems Compuaional

More information

Inventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions

Inventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions Muli-Period Sochasic Models: Opimali of (s, S) Polic for -Convex Objecive Funcions Consider a seing similar o he N-sage newsvendor problem excep ha now here is a fixed re-ordering cos (> 0) for each (re-)order.

More information

Let us start with a two dimensional case. We consider a vector ( x,

Let us start with a two dimensional case. We consider a vector ( x, Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our

More information

Analysis of Microstrip Coupling Gap to Estimate Polymer Permittivity

Analysis of Microstrip Coupling Gap to Estimate Polymer Permittivity Analysis of Microsrip Couplin Gap o Esimae Polymer Permiiviy Chanchal Yadav Deparmen of Physics & Elecronics Rajdhani Collee, Universiy of Delhi Delhi, India Absrac A ap in he microsrip line can be modeled

More information

The Arcsine Distribution

The Arcsine Distribution The Arcsine Disribuion Chris H. Rycrof Ocober 6, 006 A common heme of he class has been ha he saisics of single walker are ofen very differen from hose of an ensemble of walkers. On he firs homework, we

More information

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t... Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger

More information

5. Stochastic processes (1)

5. Stochastic processes (1) Lec05.pp S-38.45 - Inroducion o Teleraffic Theory Spring 2005 Conens Basic conceps Poisson process 2 Sochasic processes () Consider some quaniy in a eleraffic (or any) sysem I ypically evolves in ime randomly

More information

Robotics I. April 11, The kinematics of a 3R spatial robot is specified by the Denavit-Hartenberg parameters in Tab. 1.

Robotics I. April 11, The kinematics of a 3R spatial robot is specified by the Denavit-Hartenberg parameters in Tab. 1. Roboics I April 11, 017 Exercise 1 he kinemaics of a 3R spaial robo is specified by he Denavi-Harenberg parameers in ab 1 i α i d i a i θ i 1 π/ L 1 0 1 0 0 L 3 0 0 L 3 3 able 1: able of DH parameers of

More information

SOLUTIONS TO ECE 3084

SOLUTIONS TO ECE 3084 SOLUTIONS TO ECE 384 PROBLEM 2.. For each sysem below, specify wheher or no i is: (i) memoryless; (ii) causal; (iii) inverible; (iv) linear; (v) ime invarian; Explain your reasoning. If he propery is no

More information

Timed Circuits. Asynchronous Circuit Design. Timing Relationships. A Simple Example. Timed States. Timing Sequences. ({r 6 },t6 = 1.

Timed Circuits. Asynchronous Circuit Design. Timing Relationships. A Simple Example. Timed States. Timing Sequences. ({r 6 },t6 = 1. Timed Circuis Asynchronous Circui Design Chris J. Myers Lecure 7: Timed Circuis Chaper 7 Previous mehods only use limied knowledge of delays. Very robus sysems, bu exremely conservaive. Large funcional

More information

) were both constant and we brought them from under the integral.

) were both constant and we brought them from under the integral. YIELD-PER-RECRUIT (coninued The yield-per-recrui model applies o a cohor, bu we saw in he Age Disribuions lecure ha he properies of a cohor do no apply in general o a collecion of cohors, which is wha

More information

non -negative cone Population dynamics motivates the study of linear models whose coefficient matrices are non-negative or positive.

non -negative cone Population dynamics motivates the study of linear models whose coefficient matrices are non-negative or positive. LECTURE 3 Linear/Nonnegaive Marix Models x ( = Px ( A= m m marix, x= m vecor Linear sysems of difference equaions arise in several difference conexs: Linear approximaions (linearizaion Perurbaion analysis

More information

11!Hí MATHEMATICS : ERDŐS AND ULAM PROC. N. A. S. of decomposiion, properly speaking) conradics he possibiliy of defining a counably addiive real-valu

11!Hí MATHEMATICS : ERDŐS AND ULAM PROC. N. A. S. of decomposiion, properly speaking) conradics he possibiliy of defining a counably addiive real-valu ON EQUATIONS WITH SETS AS UNKNOWNS BY PAUL ERDŐS AND S. ULAM DEPARTMENT OF MATHEMATICS, UNIVERSITY OF COLORADO, BOULDER Communicaed May 27, 1968 We shall presen here a number of resuls in se heory concerning

More information

In this chapter the model of free motion under gravity is extended to objects projected at an angle. When you have completed it, you should

In this chapter the model of free motion under gravity is extended to objects projected at an angle. When you have completed it, you should Cambridge Universiy Press 978--36-60033-7 Cambridge Inernaional AS and A Level Mahemaics: Mechanics Coursebook Excerp More Informaion Chaper The moion of projeciles In his chaper he model of free moion

More information

INTRODUCTION TO MACHINE LEARNING 3RD EDITION

INTRODUCTION TO MACHINE LEARNING 3RD EDITION ETHEM ALPAYDIN The MIT Press, 2014 Lecure Slides for INTRODUCTION TO MACHINE LEARNING 3RD EDITION alpaydin@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/i2ml3e CHAPTER 2: SUPERVISED LEARNING Learning a Class

More information

Notes on Kalman Filtering

Notes on Kalman Filtering Noes on Kalman Filering Brian Borchers and Rick Aser November 7, Inroducion Daa Assimilaion is he problem of merging model predicions wih acual measuremens of a sysem o produce an opimal esimae of he curren

More information

MATH 128A, SUMMER 2009, FINAL EXAM SOLUTION

MATH 128A, SUMMER 2009, FINAL EXAM SOLUTION MATH 28A, SUMME 2009, FINAL EXAM SOLUTION BENJAMIN JOHNSON () (8 poins) [Lagrange Inerpolaion] (a) (4 poins) Le f be a funcion defined a some real numbers x 0,..., x n. Give a defining equaion for he Lagrange

More information

Georey E. Hinton. University oftoronto. Technical Report CRG-TR February 22, Abstract

Georey E. Hinton. University oftoronto.   Technical Report CRG-TR February 22, Abstract Parameer Esimaion for Linear Dynamical Sysems Zoubin Ghahramani Georey E. Hinon Deparmen of Compuer Science Universiy oftorono 6 King's College Road Torono, Canada M5S A4 Email: zoubin@cs.orono.edu Technical

More information

We just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n

We just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n Lecure 3 - Kövari-Sós-Turán Theorem Jacques Versraëe jacques@ucsd.edu We jus finished he Erdős-Sone Theorem, and ex(n, F ) ( /(χ(f ) )) ( n 2). So we have asympoics when χ(f ) 3 bu no when χ(f ) = 2 i.e.

More information

Math 10B: Mock Mid II. April 13, 2016

Math 10B: Mock Mid II. April 13, 2016 Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.

More information

Chapter 3 Boundary Value Problem

Chapter 3 Boundary Value Problem Chaper 3 Boundary Value Problem A boundary value problem (BVP) is a problem, ypically an ODE or a PDE, which has values assigned on he physical boundary of he domain in which he problem is specified. Le

More information

Lecture Notes 2. The Hilbert Space Approach to Time Series

Lecture Notes 2. The Hilbert Space Approach to Time Series Time Series Seven N. Durlauf Universiy of Wisconsin. Basic ideas Lecure Noes. The Hilber Space Approach o Time Series The Hilber space framework provides a very powerful language for discussing he relaionship

More information

EECE251. Circuit Analysis I. Set 4: Capacitors, Inductors, and First-Order Linear Circuits

EECE251. Circuit Analysis I. Set 4: Capacitors, Inductors, and First-Order Linear Circuits EEE25 ircui Analysis I Se 4: apaciors, Inducors, and Firs-Order inear ircuis Shahriar Mirabbasi Deparmen of Elecrical and ompuer Engineering Universiy of Briish olumbia shahriar@ece.ubc.ca Overview Passive

More information

A generalization of the Burg s algorithm to periodically correlated time series

A generalization of the Burg s algorithm to periodically correlated time series A generalizaion of he Burg s algorihm o periodically correlaed ime series Georgi N. Boshnakov Insiue of Mahemaics, Bulgarian Academy of Sciences ABSTRACT In his paper periodically correlaed processes are

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Linear Response Theory: The connecion beween QFT and experimens 3.1. Basic conceps and ideas Q: How do we measure he conduciviy of a meal? A: we firs inroduce a weak elecric field E, and

More information

BBP-type formulas, in general bases, for arctangents of real numbers

BBP-type formulas, in general bases, for arctangents of real numbers Noes on Number Theory and Discree Mahemaics Vol. 19, 13, No. 3, 33 54 BBP-ype formulas, in general bases, for arcangens of real numbers Kunle Adegoke 1 and Olawanle Layeni 2 1 Deparmen of Physics, Obafemi

More information

Math 334 Fall 2011 Homework 11 Solutions

Math 334 Fall 2011 Homework 11 Solutions Dec. 2, 2 Mah 334 Fall 2 Homework Soluions Basic Problem. Transform he following iniial value problem ino an iniial value problem for a sysem: u + p()u + q() u g(), u() u, u () v. () Soluion. Le v u. Then

More information

Online Appendix for "Customer Recognition in. Experience versus Inspection Good Markets"

Online Appendix for Customer Recognition in. Experience versus Inspection Good Markets Online Appendix for "Cusomer Recogniion in Experience versus Inspecion Good Markes" Bing Jing Cheong Kong Graduae School of Business Beijing, 0078, People s Republic of China, bjing@ckgsbeducn November

More information

The Asymptotic Behavior of Nonoscillatory Solutions of Some Nonlinear Dynamic Equations on Time Scales

The Asymptotic Behavior of Nonoscillatory Solutions of Some Nonlinear Dynamic Equations on Time Scales Advances in Dynamical Sysems and Applicaions. ISSN 0973-5321 Volume 1 Number 1 (2006, pp. 103 112 c Research India Publicaions hp://www.ripublicaion.com/adsa.hm The Asympoic Behavior of Nonoscillaory Soluions

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION DOI: 0.038/NCLIMATE893 Temporal resoluion and DICE * Supplemenal Informaion Alex L. Maren and Sephen C. Newbold Naional Cener for Environmenal Economics, US Environmenal Proecion

More information

Generalized Least Squares

Generalized Least Squares Generalized Leas Squares Augus 006 1 Modified Model Original assumpions: 1 Specificaion: y = Xβ + ε (1) Eε =0 3 EX 0 ε =0 4 Eεε 0 = σ I In his secion, we consider relaxing assumpion (4) Insead, assume

More information

Refinement of Document Clustering by Using NMF *

Refinement of Document Clustering by Using NMF * Refinemen of Documen Clusering by Using NMF * Hiroyuki Shinnou and Minoru Sasaki Deparmen of Compuer and Informaion Sciences, Ibaraki Universiy, 4-12-1 Nakanarusawa, Hiachi, Ibaraki JAPAN 316-8511 {shinnou,

More information

Families with no matchings of size s

Families with no matchings of size s Families wih no machings of size s Peer Franl Andrey Kupavsii Absrac Le 2, s 2 be posiive inegers. Le be an n-elemen se, n s. Subses of 2 are called families. If F ( ), hen i is called - uniform. Wha is

More information

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Simulaion-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Week Descripion Reading Maerial 2 Compuer Simulaion of Dynamic Models Finie Difference, coninuous saes, discree ime Simple Mehods Euler Trapezoid

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

Most Probable Phase Portraits of Stochastic Differential Equations and Its Numerical Simulation

Most Probable Phase Portraits of Stochastic Differential Equations and Its Numerical Simulation Mos Probable Phase Porrais of Sochasic Differenial Equaions and Is Numerical Simulaion Bing Yang, Zhu Zeng and Ling Wang 3 School of Mahemaics and Saisics, Huazhong Universiy of Science and Technology,

More information

ODEs II, Lecture 1: Homogeneous Linear Systems - I. Mike Raugh 1. March 8, 2004

ODEs II, Lecture 1: Homogeneous Linear Systems - I. Mike Raugh 1. March 8, 2004 ODEs II, Lecure : Homogeneous Linear Sysems - I Mike Raugh March 8, 4 Inroducion. In he firs lecure we discussed a sysem of linear ODEs for modeling he excreion of lead from he human body, saw how o ransform

More information

m = 41 members n = 27 (nonfounders), f = 14 (founders) 8 markers from chromosome 19

m = 41 members n = 27 (nonfounders), f = 14 (founders) 8 markers from chromosome 19 Sequenial Imporance Sampling (SIS) AKA Paricle Filering, Sequenial Impuaion (Kong, Liu, Wong, 994) For many problems, sampling direcly from he arge disribuion is difficul or impossible. One reason possible

More information

Solutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore

Solutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore Soluions of Sample Problems for Third In-Class Exam Mah 6, Spring, Professor David Levermore Compue he Laplace ransform of f e from is definiion Soluion The definiion of he Laplace ransform gives L[f]s

More information

Math 315: Linear Algebra Solutions to Assignment 6

Math 315: Linear Algebra Solutions to Assignment 6 Mah 35: Linear Algebra s o Assignmen 6 # Which of he following ses of vecors are bases for R 2? {2,, 3, }, {4,, 7, 8}, {,,, 3}, {3, 9, 4, 2}. Explain your answer. To generae he whole R 2, wo linearly independen

More information

Global Synchronization of Directed Networks with Fast Switching Topologies

Global Synchronization of Directed Networks with Fast Switching Topologies Commun. Theor. Phys. (Beijing, China) 52 (2009) pp. 1019 1924 c Chinese Physical Sociey and IOP Publishing Ld Vol. 52, No. 6, December 15, 2009 Global Synchronizaion of Direced Neworks wih Fas Swiching

More information

EXPLICIT TIME INTEGRATORS FOR NONLINEAR DYNAMICS DERIVED FROM THE MIDPOINT RULE

EXPLICIT TIME INTEGRATORS FOR NONLINEAR DYNAMICS DERIVED FROM THE MIDPOINT RULE Version April 30, 2004.Submied o CTU Repors. EXPLICIT TIME INTEGRATORS FOR NONLINEAR DYNAMICS DERIVED FROM THE MIDPOINT RULE Per Krysl Universiy of California, San Diego La Jolla, California 92093-0085,

More information

SPECTRAL EVOLUTION OF A ONE PARAMETER EXTENSION OF A REAL SYMMETRIC TOEPLITZ MATRIX* William F. Trench. SIAM J. Matrix Anal. Appl. 11 (1990),

SPECTRAL EVOLUTION OF A ONE PARAMETER EXTENSION OF A REAL SYMMETRIC TOEPLITZ MATRIX* William F. Trench. SIAM J. Matrix Anal. Appl. 11 (1990), SPECTRAL EVOLUTION OF A ONE PARAMETER EXTENSION OF A REAL SYMMETRIC TOEPLITZ MATRIX* William F Trench SIAM J Marix Anal Appl 11 (1990), 601-611 Absrac Le T n = ( i j ) n i,j=1 (n 3) be a real symmeric

More information

Application of a Stochastic-Fuzzy Approach to Modeling Optimal Discrete Time Dynamical Systems by Using Large Scale Data Processing

Application of a Stochastic-Fuzzy Approach to Modeling Optimal Discrete Time Dynamical Systems by Using Large Scale Data Processing Applicaion of a Sochasic-Fuzzy Approach o Modeling Opimal Discree Time Dynamical Sysems by Using Large Scale Daa Processing AA WALASZE-BABISZEWSA Deparmen of Compuer Engineering Opole Universiy of Technology

More information

Spring Ammar Abu-Hudrouss Islamic University Gaza

Spring Ammar Abu-Hudrouss Islamic University Gaza Chaper 7 Reed-Solomon Code Spring 9 Ammar Abu-Hudrouss Islamic Universiy Gaza ١ Inroducion A Reed Solomon code is a special case of a BCH code in which he lengh of he code is one less han he size of he

More information

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still.

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still. Lecure - Kinemaics in One Dimension Displacemen, Velociy and Acceleraion Everyhing in he world is moving. Nohing says sill. Moion occurs a all scales of he universe, saring from he moion of elecrons in

More information

Tracking Adversarial Targets

Tracking Adversarial Targets A. Proofs Proof of Lemma 3. Consider he Bellman equaion λ + V π,l x, a lx, a + V π,l Ax + Ba, πax + Ba. We prove he lemma by showing ha he given quadraic form is he unique soluion of he Bellman equaion.

More information

GMM - Generalized Method of Moments

GMM - Generalized Method of Moments GMM - Generalized Mehod of Momens Conens GMM esimaion, shor inroducion 2 GMM inuiion: Maching momens 2 3 General overview of GMM esimaion. 3 3. Weighing marix...........................................

More information

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model Modal idenificaion of srucures from roving inpu daa by means of maximum likelihood esimaion of he sae space model J. Cara, J. Juan, E. Alarcón Absrac The usual way o perform a forced vibraion es is o fix

More information

Analyze patterns and relationships. 3. Generate two numerical patterns using AC

Analyze patterns and relationships. 3. Generate two numerical patterns using AC envision ah 2.0 5h Grade ah Curriculum Quarer 1 Quarer 2 Quarer 3 Quarer 4 andards: =ajor =upporing =Addiional Firs 30 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 andards: Operaions and Algebraic Thinking

More information

Some Ramsey results for the n-cube

Some Ramsey results for the n-cube Some Ramsey resuls for he n-cube Ron Graham Universiy of California, San Diego Jozsef Solymosi Universiy of Briish Columbia, Vancouver, Canada Absrac In his noe we esablish a Ramsey-ype resul for cerain

More information

Computer-Aided Analysis of Electronic Circuits Course Notes 3

Computer-Aided Analysis of Electronic Circuits Course Notes 3 Gheorghe Asachi Technical Universiy of Iasi Faculy of Elecronics, Telecommunicaions and Informaion Technologies Compuer-Aided Analysis of Elecronic Circuis Course Noes 3 Bachelor: Telecommunicaion Technologies

More information

Adaptation and Synchronization over a Network: stabilization without a reference model

Adaptation and Synchronization over a Network: stabilization without a reference model Adapaion and Synchronizaion over a Nework: sabilizaion wihou a reference model Travis E. Gibson (gibson@mi.edu) Harvard Medical School Deparmen of Pahology, Brigham and Women s Hospial 55 h Conference

More information

Longest Common Prefixes

Longest Common Prefixes Longes Common Prefixes The sandard ordering for srings is he lexicographical order. I is induced by an order over he alphabe. We will use he same symbols (,

More information

Stochastic Structural Dynamics. Lecture-6

Stochastic Structural Dynamics. Lecture-6 Sochasic Srucural Dynamics Lecure-6 Random processes- Dr C S Manohar Deparmen of Civil Engineering Professor of Srucural Engineering Indian Insiue of Science Bangalore 560 0 India manohar@civil.iisc.erne.in

More information

An introduction to the theory of SDDP algorithm

An introduction to the theory of SDDP algorithm An inroducion o he heory of SDDP algorihm V. Leclère (ENPC) Augus 1, 2014 V. Leclère Inroducion o SDDP Augus 1, 2014 1 / 21 Inroducion Large scale sochasic problem are hard o solve. Two ways of aacking

More information

System of Linear Differential Equations

System of Linear Differential Equations Sysem of Linear Differenial Equaions In "Ordinary Differenial Equaions" we've learned how o solve a differenial equaion for a variable, such as: y'k5$e K2$x =0 solve DE yx = K 5 2 ek2 x C_C1 2$y''C7$y

More information

2. Nonlinear Conservation Law Equations

2. Nonlinear Conservation Law Equations . Nonlinear Conservaion Law Equaions One of he clear lessons learned over recen years in sudying nonlinear parial differenial equaions is ha i is generally no wise o ry o aack a general class of nonlinear

More information

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 17

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 17 EES 16A Designing Informaion Devices and Sysems I Spring 019 Lecure Noes Noe 17 17.1 apaciive ouchscreen In he las noe, we saw ha a capacior consiss of wo pieces on conducive maerial separaed by a nonconducive

More information

On a problem of Graham By E. ERDŐS and E. SZEMERÉDI (Budapest) GRAHAM stated the following conjecture : Let p be a prime and a 1,..., ap p non-zero re

On a problem of Graham By E. ERDŐS and E. SZEMERÉDI (Budapest) GRAHAM stated the following conjecture : Let p be a prime and a 1,..., ap p non-zero re On a roblem of Graham By E. ERDŐS and E. SZEMERÉDI (Budaes) GRAHAM saed he following conjecure : Le be a rime and a 1,..., a non-zero residues (mod ). Assume ha if ' a i a i, ei=0 or 1 (no all e i=0) is

More information

Lecture Notes 3: Quantitative Analysis in DSGE Models: New Keynesian Model

Lecture Notes 3: Quantitative Analysis in DSGE Models: New Keynesian Model Lecure Noes 3: Quaniaive Analysis in DSGE Models: New Keynesian Model Zhiwei Xu, Email: xuzhiwei@sju.edu.cn The moneary policy plays lile role in he basic moneary model wihou price sickiness. We now urn

More information

arxiv:cond-mat/ May 2002

arxiv:cond-mat/ May 2002 -- uadrupolar Glass Sae in para-hydrogen and orho-deuerium under pressure. T.I.Schelkacheva. arxiv:cond-ma/5538 6 May Insiue for High Pressure Physics, Russian Academy of Sciences, Troisk 49, Moscow Region,

More information

Sequential Importance Resampling (SIR) Particle Filter

Sequential Importance Resampling (SIR) Particle Filter Paricle Filers++ Pieer Abbeel UC Berkeley EECS Many slides adaped from Thrun, Burgard and Fox, Probabilisic Roboics 1. Algorihm paricle_filer( S -1, u, z ): 2. Sequenial Imporance Resampling (SIR) Paricle

More information

Failure of the work-hamiltonian connection for free energy calculations. Abstract

Failure of the work-hamiltonian connection for free energy calculations. Abstract Failure of he work-hamilonian connecion for free energy calculaions Jose M. G. Vilar 1 and J. Miguel Rubi 1 Compuaional Biology Program, Memorial Sloan-Keering Cancer Cener, 175 York Avenue, New York,

More information

Empirical Process Theory

Empirical Process Theory Empirical Process heory 4.384 ime Series Analysis, Fall 27 Reciaion by Paul Schrimpf Supplemenary o lecures given by Anna Mikusheva Ocober 7, 28 Reciaion 7 Empirical Process heory Le x be a real-valued

More information

Some Basic Information about M-S-D Systems

Some Basic Information about M-S-D Systems Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,

More information

Solutions to the Exam Digital Communications I given on the 11th of June = 111 and g 2. c 2

Solutions to the Exam Digital Communications I given on the 11th of June = 111 and g 2. c 2 Soluions o he Exam Digial Communicaions I given on he 11h of June 2007 Quesion 1 (14p) a) (2p) If X and Y are independen Gaussian variables, hen E [ XY ]=0 always. (Answer wih RUE or FALSE) ANSWER: False.

More information

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n Module Fick s laws of diffusion Fick s laws of diffusion and hin film soluion Adolf Fick (1855) proposed: d J α d d d J (mole/m s) flu (m /s) diffusion coefficien and (mole/m 3 ) concenraion of ions, aoms

More information

Matrix Versions of Some Refinements of the Arithmetic-Geometric Mean Inequality

Matrix Versions of Some Refinements of the Arithmetic-Geometric Mean Inequality Marix Versions of Some Refinemens of he Arihmeic-Geomeric Mean Inequaliy Bao Qi Feng and Andrew Tonge Absrac. We esablish marix versions of refinemens due o Alzer ], Carwrigh and Field 4], and Mercer 5]

More information

Chapter 3 (Lectures 12, 13 and 14) Longitudinal stick free static stability and control

Chapter 3 (Lectures 12, 13 and 14) Longitudinal stick free static stability and control Fligh dynamics II Sabiliy and conrol haper 3 (Lecures 1, 13 and 14) Longiudinal sick free saic sabiliy and conrol Keywords : inge momen and is variaion wih ail angle, elevaor deflecion and ab deflecion

More information

Economics 8105 Macroeconomic Theory Recitation 6

Economics 8105 Macroeconomic Theory Recitation 6 Economics 8105 Macroeconomic Theory Reciaion 6 Conor Ryan Ocober 11h, 2016 Ouline: Opimal Taxaion wih Governmen Invesmen 1 Governmen Expendiure in Producion In hese noes we will examine a model in which

More information

Supplement for Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence

Supplement for Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence Supplemen for Sochasic Convex Opimizaion: Faser Local Growh Implies Faser Global Convergence Yi Xu Qihang Lin ianbao Yang Proof of heorem heorem Suppose Assumpion holds and F (w) obeys he LGC (6) Given

More information

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3 and d = c b - b c c d = c b - b c c This process is coninued unil he nh row has been compleed. The complee array of coefficiens is riangular. Noe ha in developing he array an enire row may be divided or

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

Testing the Random Walk Model. i.i.d. ( ) r

Testing the Random Walk Model. i.i.d. ( ) r he random walk heory saes: esing he Random Walk Model µ ε () np = + np + Momen Condiions where where ε ~ i.i.d he idea here is o es direcly he resricions imposed by momen condiions. lnp lnp µ ( lnp lnp

More information

Random Walk with Anti-Correlated Steps

Random Walk with Anti-Correlated Steps Random Walk wih Ani-Correlaed Seps John Noga Dirk Wagner 2 Absrac We conjecure he expeced value of random walks wih ani-correlaed seps o be exacly. We suppor his conjecure wih 2 plausibiliy argumens and

More information

L p -L q -Time decay estimate for solution of the Cauchy problem for hyperbolic partial differential equations of linear thermoelasticity

L p -L q -Time decay estimate for solution of the Cauchy problem for hyperbolic partial differential equations of linear thermoelasticity ANNALES POLONICI MATHEMATICI LIV.2 99) L p -L q -Time decay esimae for soluion of he Cauchy problem for hyperbolic parial differenial equaions of linear hermoelasiciy by Jerzy Gawinecki Warszawa) Absrac.

More information

Concourse Math Spring 2012 Worked Examples: Matrix Methods for Solving Systems of 1st Order Linear Differential Equations

Concourse Math Spring 2012 Worked Examples: Matrix Methods for Solving Systems of 1st Order Linear Differential Equations Concourse Mah 80 Spring 0 Worked Examples: Marix Mehods for Solving Sysems of s Order Linear Differenial Equaions The Main Idea: Given a sysem of s order linear differenial equaions d x d Ax wih iniial

More information

Cash Flow Valuation Mode Lin Discrete Time

Cash Flow Valuation Mode Lin Discrete Time IOSR Journal of Mahemaics (IOSR-JM) e-issn: 2278-5728,p-ISSN: 2319-765X, 6, Issue 6 (May. - Jun. 2013), PP 35-41 Cash Flow Valuaion Mode Lin Discree Time Olayiwola. M. A. and Oni, N. O. Deparmen of Mahemaics

More information

Block Diagram of a DCS in 411

Block Diagram of a DCS in 411 Informaion source Forma A/D From oher sources Pulse modu. Muliplex Bandpass modu. X M h: channel impulse response m i g i s i Digial inpu Digial oupu iming and synchronizaion Digial baseband/ bandpass

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

control properties under both Gaussian and burst noise conditions. In the ~isappointing in comparison with convolutional code systems designed

control properties under both Gaussian and burst noise conditions. In the ~isappointing in comparison with convolutional code systems designed 535 SOFT-DECSON THRESHOLD DECODNG OF CONVOLUTONAL CODES R.M.F. Goodman*, B.Sc., Ph.D. W.H. Ng*, M.S.E.E. Sunnnary Exising majoriy-decision hreshold decoders have so far been limied o his paper a new mehod

More information

8. Basic RL and RC Circuits

8. Basic RL and RC Circuits 8. Basic L and C Circuis This chaper deals wih he soluions of he responses of L and C circuis The analysis of C and L circuis leads o a linear differenial equaion This chaper covers he following opics

More information

Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2

Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2 Financial Economerics Kalman Filer: some applicaions o Finance Universiy of Evry - Maser 2 Eric Bouyé January 27, 2009 Conens 1 Sae-space models 2 2 The Scalar Kalman Filer 2 21 Presenaion 2 22 Summary

More information

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1 Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies

More information

3, so θ = arccos

3, so θ = arccos Mahemaics 210 Professor Alan H Sein Monday, Ocober 1, 2007 SOLUTIONS This problem se is worh 50 poins 1 Find he angle beween he vecors (2, 7, 3) and (5, 2, 4) Soluion: Le θ be he angle (2, 7, 3) (5, 2,

More information

Written HW 9 Sol. CS 188 Fall Introduction to Artificial Intelligence

Written HW 9 Sol. CS 188 Fall Introduction to Artificial Intelligence CS 188 Fall 2018 Inroducion o Arificial Inelligence Wrien HW 9 Sol. Self-assessmen due: Tuesday 11/13/2018 a 11:59pm (submi via Gradescope) For he self assessmen, fill in he self assessmen boxes in your

More information

t j i, and then can be naturally extended to K(cf. [S-V]). The Hasse derivatives satisfy the following: is defined on k(t) by D (i)

t j i, and then can be naturally extended to K(cf. [S-V]). The Hasse derivatives satisfy the following: is defined on k(t) by D (i) A NOTE ON WRONSKIANS AND THE ABC THEOREM IN FUNCTION FIELDS OF RIME CHARACTERISTIC Julie Tzu-Yueh Wang Insiue of Mahemaics Academia Sinica Nankang, Taipei 11529 Taiwan, R.O.C. May 14, 1998 Absrac. We provide

More information

Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds

Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Kriging Models Predicing Arazine Concenraions in Surface Waer Draining Agriculural Waersheds Paul L. Mosquin, Jeremy Aldworh, Wenlin Chen Supplemenal Maerial Number

More information

APPENDIX AVAILABLE ON THE HEI WEB SITE

APPENDIX AVAILABLE ON THE HEI WEB SITE APPENDIX AVAILABLE ON HE HEI WEB SIE Research Repor 83 Developmen of Saisical Mehods for Mulipolluan Research Par. Developmen of Enhanced Saisical Mehods for Assessing Healh Effecs Associaed wih an Unknown

More information