Non-Cooperative Games
|
|
- Roy Wilcox
- 5 years ago
- Views:
Transcription
1 N-Cperatve Games a ucerta evrmet Rger J-B Wets rbwets@ucavs.eu Uverst Calra, Davs ONR-MURI, Jul 2002 p.1/??
2 I. Determstc Vers ONR-MURI, Jul 2002 p.2/??
3 Fg a Nash-equlbrum prblem rmulat the Nash-uct asscate wth a game max- pts a Nash equlbrum pts remars abut cmputatal schemes ONR-MURI, Jul 2002 p.3/??
4 Aget s Prblem te (tw?), agets:, ecs aget, ecss all ther agets, -perrmace c : Nash Equlbrum: such that r all ONR-MURI, Jul 2002 p.4/??
5 Startg pst B G ONR-MURI, Jul 2002 p./??
6 . %$ -, + )( ONR-MURI, Jul 2002 p.6/?? Mathematcal Mel -a a $ ' %$ %$ # "! %$ %$ # "! %$ / %$ # "!
7 -perrmace uct u a, x a - z -a ONR-MURI, Jul 2002 p.7/??
8 7 7 The Nash-uct 6! 8 r sme a therwse ONR-MURI, Jul 2002 p.8/??
9 ; : - Equlbrum a max- pts s a Nash equlbrum wth a l = < 6! ; 3 $ 3 : < 6! $ = < 6! ; 3 $ 3 : s a argmax- pt the Nash-uct..e., 6! > ONR-MURI, Jul 2002 p./??
10 I I I C C Q R Exstece Nash Equlbrum L I FHGE ANM E I L I K J I argmax-. exstece exstece, cvex s usc s lsc s usc 7 usc ccave. S R cvex cvex ONR-MURI, Jul 2002 p.10/??
11 Nash equlbrum ONR-MURI, Jul 2002 p.11/??
12 E I I K J I U T T T T Stablt Nash Equlbrum ANM E I C L I A L I I C ANM stablt Nash Equlbrum = stablt argmax- Nash-uct Nash-ucts lpse cvergece Nash-cs cvergece argmax- pts! lpse cvergece bvarate ucts. lpse cvergece s relate t ep-cvergece. (uvarate) ucts ONR-MURI, Jul 2002 p.12/??
13 > \ [ Z Z Y Y ' Z Z Z R R Augmete Nash-uct PL-hmtp meths, lw mesal ptmzat-base meth va augmetat - = sale pt V W Zba ' Z = X ^`_ (] V W where a Zba are ual rms. ONR-MURI, Jul 2002 p.13/??
14 c V Z Y ' ' W _ Z Y ' ' _ Z Y ' ' _ h Y Iterats 6!. Set S 6! Za ^ (] be Za ^ (] g! 3 Za be be ^ (] be g! max- pt., as ONR-MURI, Jul 2002 p.14/??
15 II. Stchastc Evrmet ONR-MURI, Jul 2002 p.1/??
16 Exstece, Algrthms -cperatve a ucerta evrmet the agets ptmzat prblems rmat lw -atcpatvt stegrat the stchastc prblem Nash-cs asscate wth a stchastc game remars abut exstece, cmputatal prceures ONR-MURI, Jul 2002 p.16/??
17 Frmulat ucerta (stchastc) evrmet ecs tme 1 (w) ml ecs tme 2 l perrmace estmate aget Nash Equlbrum: R such that r all : R -perrmace estmate ONR-MURI, Jul 2002 p.17/??
18 < $ + )( Aget s Prblem t l t q ;sr p 7 t ml ONR-MURI, Jul 2002 p.18/??
19 < $ + )( Aget s Prblem t l t q ;sr p 7 t ml a tw-stage stchastc ptmzat prblem (geeralzes t -stage, amcall) ONR-MURI, Jul 2002 p.18/??
20 $ + )( 7 Aget s Prblem t l t q ;sr < p t ml a tw-stage stchastc ptmzat prblem (geeralzes t -stage, amcall) Nte: strbut es t epe but the strbut the state the sstem es ONR-MURI, Jul 2002 p.18/??
21 l + )( 7 u u v v Stchastc Optmzat < q;sr p $ l Decs prcess: ecs bservat recurse Irmat prcess: rmat abut the uture s avalable t ca ttall epe realzat.e., there s a -atcpatvt restrct. ONR-MURI, Jul 2002 p.1/??
22 Remvg -atcpatvt: p;sr < $ q;sr < l )( + ml 7 w 7 w x Wth a cstrat qualcat, Q multplers R p such that - a p $ q [ \ l )( + l 7 has the same slut wth R = cstat. ONR-MURI, Jul 2002 p.20/??
23 DISINTEGRATION Oe ca slve: p; r < $ q; r < [ \ l )( + l 7 b slvg r each : p $ q [ \ l )( + ml wth p q, ONR-MURI, Jul 2002 p.21/??
24 z z - { - Prgressve hegg algrthm Step 0. pc R wth,. ONR-MURI, Jul 2002 p.22/??
25 z - { - $ \ [ g Prgressve hegg algrthm., z wth, R $ Step 0. pc Step 1. r each l p< q;}~ g q! p $ p! ONR-MURI, Jul 2002 p.22/??
26 z - { - $ \ [ g Z ƒ {, Prgressve hegg algrthm., z wth, R $ Step 0. pc Step 1. r each l p< q;}~ g q! p $ p! $ $ Step 2. set Z $ $ }! Stp $ $ therwse, a retur t Step 1. wth be ONR-MURI, Jul 2002 p.22/??
27 z - { - $ \ [ g Z ƒ {, Z { Prgressve hegg algrthm., z wth, R $ Step 0. pc Step 1. r each l p< q;}~ g q! p $ p! $ $ Step 2. set Z $ $ }! Stp $ $ therwse, a retur t Step 1. wth be Cvergece Z $ 8 a a prxmal term $ ONR-MURI, Jul 2002 p.22/?? lear cvergece
28 - [ $ + )( Dstegrate equlbrum: ;, let r r each, the etermstc Nash equlbrum whe the agets prblems are: t l \ t q p t ml ONR-MURI, Jul 2002 p.23/??
29 \ [ R Exstece a Algrthm(s) Exstece: ee Nash-ucts r stegrate prblems (=> exstece) a use stablt Nash equlbrum w.r.t. perturbats ( ) Slut Prceure: verall strateg the Prgressve Hegg algrthm t bta cvergece the (-atcpatvt) multplers. Step 1 PHa, terate Augmete Nash-uct t bta argmax- pt. ONR-MURI, Jul 2002 p.24/??
Ordinary Differential Equations. Orientation. Lesson Objectives. Ch. 25. ODE s. Runge-Kutta Methods. Motivation Mathematical Background
Ordar Deretal Equats C. 5 Oretat ODE s Mtvat Matematcal Bacgrud Ruge-Kutta Metds Euler s Metd Hue ad Mdpt metds Less Objectves Be able t class ODE s ad dstgus ODE s rm PDE s. Be able t reduce t rder ODE
More informationThe Simple Linear Regression Model: Theory
Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79
More informationModified Firefly Algorithm in Solving Economic Dispatch Problems with Practical Constraints
2012 IEEE Iteratal Cferece wer ad Eergy (EC), 2-5 December 2012, Kta Kabalu Sabah, Malaysa Mdfed Frefly Algrthm Slvg Ecmc Dspatch rblems wth ractcal Cstrats Mhd Herwa Sulama, Hamda Dayal Faculty f Electrcal
More informationPY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid
The Ide Ellpsd M.P. Vaugha Learg bjectves Wave prpagat astrpc meda Ptg walk-ff The de ellpsd Brefrgece 1 Wave prpagat astrpc meda The wave equat Relatve permttvt I E. Assumg free charges r currets E. Substtutg
More informationData Mining: Concepts and Techniques
Data Mg: cepts ad Techques 3 rd ed. hapter 10 1 Evaluat f lusterg lusterg evaluat assesses the feasblty f clusterg aalyss a data set ad the qualty f the results geerated by a clusterg methd. Three mar
More informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More informationPart a: Writing the nodal equations and solving for v o gives the magnitude and phase response: tan ( 0.25 )
+ - Hmewrk 0 Slutin ) In the circuit belw: a. Find the magnitude and phase respnse. b. What kind f filter is it? c. At what frequency is the respnse 0.707 if the generatr has a ltage f? d. What is the
More informationObjective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.
CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml
More informationSteady State Conduction
ECE309 Intrductin t Thermdynamics and Heat Transfer Spring 005 Tutrial # 7 Steady State Cnductin Prblem 1 Cnsider a naked persn standing in a rm at 0 C with an expsed surface area f 17m The deep bdy temperature
More informationLecture 2. Basic Semiconductor Physics
Lecture Basc Semcductr Physcs I ths lecture yu wll lear: What are semcductrs? Basc crystal structure f semcductrs Electrs ad hles semcductrs Itrsc semcductrs Extrsc semcductrs -ded ad -ded semcductrs Semcductrs
More informationMATHEMATICAL PROGRAMMING-BASED PERTURBATION ANALYSIS FOR GI/G/1 QUEUES. He Zhang Wai Kin (Victor) Chan
Prceedgs f the 007 Wter Smulat Cferece S. G. Heders,. ller, M.-H. Hseh, J. Shrtle, J. D. ew, ad R. R. art, eds. MAHEMAICAL PROGRAMMING-ASED PERURAION ANALYSIS FOR GI/G/ QUEUES He Zhag Wa K (Vctr Cha Departmet
More informationCHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol
CAPER 5 ENROPY GENERAION Istructr: Pr. Dr. Uğur Atkl Chapter 5 Etrpy Geerat (Exergy Destruct Outle st Avalable rk Cycles eat ege cycles Rergerat cycles eat pump cycles Nlw Prcesses teady-flw Prcesses Exergy
More informationNo Internal Regret via Neighborhood Watch
Uversty f Pesylvaa SchlarlyCmms Statstcs Papers Whart Faculty Research 2012 N Iteral Regret va Neghbrhd Watch Dea P. Fster Uversty f Pesylvaa Alexader Rakhl Uversty f Pesylvaa Fllw ths ad addtal wrks at:
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationm = Mass flow rate The Lonely Electron Example 0a:
The Lel Elect Exaple 0a: Mass flw ate l Liea velcit Hw fa ut f ptial eeg iteacti? Hge ucleus Bh --- 93: Uest the etu ccept. Liea etu istace eeg ( l ) l F ( tie ) ( tie ) + Like t use the peples ieas (if
More informationA New Method for Solving Integer Linear. Programming Problems with Fuzzy Variables
Appled Mathematcal Scences, Vl. 4, 00, n. 0, 997-004 A New Methd fr Slvng Integer Lnear Prgrammng Prblems wth Fuzzy Varables P. Pandan and M. Jayalakshm Department f Mathematcs, Schl f Advanced Scences,
More informationelement k Using FEM to Solve Truss Problems
sng EM t Slve Truss Prblems A truss s an engneerng structure cmpsed straght members, a certan materal, that are tpcall pn-ned at ther ends. Such members are als called tw-rce members snce the can nl transmt
More informationStudy of Correlation using Bayes Approach under bivariate Distributions
Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of
More informationPetri Net model-based distributed diagnosis for large interacting systems
Petr Net mdel-based dstrbuted dagss fr large teractg systems Gerge Jrveau ad Reé K Bel EESA-SYSTeMS Research Grup Ghet Uversty, Belgum {gergejrveau,reebel}@ugetbe Abstract The dstrbuted archtecture we
More informationSection 2 Notes. Elizabeth Stone and Charles Wang. January 15, Expectation and Conditional Expectation of a Random Variable.
Secto Notes Elzabeth Stoe ad Charles Wag Jauar 5, 9 Jot, Margal, ad Codtoal Probablt Useful Rules/Propertes. P ( x) P P ( x; ) or R f (x; ) d. P ( xj ) P (x; ) P ( ) 3. P ( x; ) P ( xj ) P ( ) 4. Baes
More informationLine Fitting and Regression
Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots
More informationELE Final Exam - Dec. 2018
ELE 509 Final Exam Dec 2018 1 Cnsider tw Gaussian randm sequences X[n] and Y[n] Assume that they are independent f each ther with means and autcvariances μ ' 3 μ * 4 C ' [m] 1 2 1 3 and C * [m] 3 1 10
More informationElshaboury SM et al.; Sch. J. Phys. Math. Stat., 2015; Vol-2; Issue-2B (Mar-May); pp
Elshabour SM et al.; Sch. J. Phs. Math. Stat. 5; Vol-; Issue-B (Mar-Ma); pp-69-75 Scholars Journal of Phscs Mathematcs Statstcs Sch. J. Phs. Math. Stat. 5; (B):69-75 Scholars Academc Scentfc Publshers
More informationQuantum Mechanics for Scientists and Engineers. David Miller
Quatum Mechaics fr Scietists ad Egieers David Miller Time-depedet perturbati thery Time-depedet perturbati thery Time-depedet perturbati basics Time-depedet perturbati thery Fr time-depedet prblems csider
More informationA Mean-Variance Portfolio Optimal Under Utility Pricing
Jural f Mathematcs ad Statstcs (4): 445-45, 6 ISSN 549-3644 6 Scece Publcats Mea-Varace Prtfl Optmal Uder Utlty Prcg Hürlma Werer Feldstrasse 45, CH-84 Zürch, Stzerlad bstract: expected utlty mdel f asset
More informationModule 7. Lecture 7: Statistical parameter estimation
Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato
More informationFourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION
Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar
More informationES201 - Examination 2 Winter Adams and Richards NAME BOX NUMBER
ES201 - Examinatin 2 Winter 2003-2004 Adams and Richards NAME BOX NUMBER Please Circle One : Richards (Perid 4) ES201-01 Adams (Perid 4) ES201-02 Adams (Perid 6) ES201-03 Prblem 1 ( 12 ) Prblem 2 ( 24
More informationCorrelation and Regression Analysis
Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the
More informationGeneralized Linear Models. Statistical Models. Classical Linear Regression Why easy formulation if complicated formulation exists?
Statstcal Models Geeralzed Lear Models Classcal lear regresso complcated formlato of smple model, strctral ad radom compoet of the model Lectre 5 Geeralzed Lear Models Geeralzed lear models geeral descrpto
More informationUNIT 7 RANK CORRELATION
UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7
More informationJig-Shape Optimization of a Flapping Wing for a Micro Air Vehicle
Jg-Shape Optmzat f a Flappg Wg fr a Mcr Ar Vehcle Jh P. Mre IV Udergraduate, Aerspace Egeerg, Uverst f Flrda The purpse f ths wrk s t develp a small rthpter capable f slw flght a dr evrmet. Mst f ths wrk
More informationSolutions to Midterm II. of the following equation consistent with the boundary condition stated u. y u x y
Sltis t Midterm II Prblem : (pts) Fid the mst geeral slti ( f the fllwig eqati csistet with the bdary cditi stated y 3 y the lie y () Slti : Sice the system () is liear the slti is give as a sperpsiti
More informationIf σis unknown. Properties of t distribution. 6.3 One and Two Sample Inferences for Means. What is the correct multiplier? t
/8/009 6.3 Oe a Tw Samle Iferece fr Mea If i kw a 95% Cfiece Iterval i 96 ±.96 96.96 ± But i ever kw. If i ukw Etimate by amle taar eviati The etimate taar errr f the mea will be / Uig the etimate taar
More informationCOV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.
c Pogsa Porchawseskul, Faculty of Ecoomcs, Chulalogkor Uversty olato of costat varace of s but they are stll depedet. C,, he error term s sad to be heteroscedastc. c Pogsa Porchawseskul, Faculty of Ecoomcs,
More informationBivariate Vieta-Fibonacci and Bivariate Vieta-Lucas Polynomials
IOSR Joural of Mathematcs (IOSR-JM) e-issn: 78-78, p-issn: 19-76X. Volume 1, Issue Ver. II (Jul. - Aug.016), PP -0 www.osrjourals.org Bvarate Veta-Fboacc ad Bvarate Veta-Lucas Polomals E. Gokce KOCER 1
More informationGASES. PV = nrt N 2 CH 4 CO 2 O 2 HCN N 2 O NO 2. Pressure & Boyle s Law Temperature & Charles s Law Avogadro s Law IDEAL GAS LAW
GASES Pressure & Byle s Law Temperature & Charles s Law Avgadr s Law IDEAL GAS LAW PV = nrt N 2 CH 4 CO 2 O 2 HCN N 2 O NO 2 Earth s atmsphere: 78% N 2 21% O 2 sme Ar, CO 2 Sme Cmmn Gasses Frmula Name
More informationL a) Calculate the maximum allowable midspan deflection (w o ) critical under which the beam will slide off its support.
ecture 6 Mderately arge Deflectin Thery f Beams Prblem 6-1: Part A: The department f Highways and Public Wrks f the state f Califrnia is in the prcess f imprving the design f bridge verpasses t meet earthquake
More informationKernels. Nuno Vasconcelos ECE Department, UCSD
Kerels Nu Vasels ECE Departmet UCSD Prpal mpet aalyss Dmesalty reut: Last tme we saw that whe the ata lves a subspae t s best t esg ur learg algrthms ths subspae D subspae 3D y φ φ λ λ y ths a be e by
More informationGenerative classification models
CS 75 Mache Learg Lecture Geeratve classfcato models Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Data: D { d, d,.., d} d, Classfcato represets a dscrete class value Goal: lear f : X Y Bar classfcato
More informationCLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs
CLASS NOTES for PBAF 58: Quattatve Methods II SPRING 005 Istructor: Jea Swaso Dael J. Evas School of Publc Affars Uversty of Washgto Ackowledgemet: The structor wshes to thak Rachel Klet, Assstat Professor,
More information" 1 = # $H vap. Chapter 3 Problems
Chapter 3 rblems rblem At 1 atmsphere pure Ge melts at 1232 K and bils at 298 K. he triple pint ccurs at =8.4x1-8 atm. Estimate the heat f vaprizatin f Ge. he heat f vaprizatin is estimated frm the Clausius
More informationCh5 Appendix Q-factor and Smith Chart Matching
h5 Appedx -factr ad mth hart Matchg 5B-1 We-ha a udwg, F rcut Desg Thery ad Applcat, hapter 8 Frequecy espse f -type Matchg Netwrks 5B- Fg.8-8 Tw desg realzats f a -type matchg etwrk.65pf, 80 f 1 GHz Fg.8-9
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationON THE LAGRANGIAN RHEONOMIC MECHANICAL SYSTEMS
THE PUBISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Vlume 1, Number 1/9, pp. ON THE AGRANGIAN RHEONOMIC MECHANICA SYSTEMS Radu MIRON*, Tmak KAWAGUCHI**, Hrak KAWAGUCHI***
More informationBME 5742 Biosystems Modeling and Control
BME 5742 Bsystems Mdeln and Cntrl Cell Electrcal Actvty: In Mvement acrss Cell Membrane and Membrane Ptental Dr. Zv Rth (FAU) 1 References Hppensteadt-Peskn, Ch. 3 Dr. Rbert Farley s lecture ntes Inc Equlbra
More informationNUMBERS, MATHEMATICS AND EQUATIONS
AUSTRALIAN CURRICULUM PHYSICS GETTING STARTED WITH PHYSICS NUMBERS, MATHEMATICS AND EQUATIONS An integral part t the understanding f ur physical wrld is the use f mathematical mdels which can be used t
More information2.3 Rectangular Components in Three-Dimensional Force Systems
2.3 Rectangular Components in Three-Dimensional Force Sstems 2.3 Rectangular Components in Three-Dimensional Force Sstems Eample 1, page 1 of 2 1. Epress the force F in terms of,, and components. F = 200
More informationExercises for Square-Congruence Modulo n ver 11
Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced
More informationUnit 14 Thermochemistry Notes
Name KEY Perid CRHS Academic Chemistry Unit 14 Thermchemistry Ntes Quiz Date Exam Date Lab Dates Ntes, Hmewrk, Exam Reviews and Their KEYS lcated n CRHS Academic Chemistry Website: https://cincchem.pbwrks.cm
More informationNAME: Prof. Ruiz. 1. [5 points] What is the difference between simple random sampling and stratified random sampling?
CS4445 ata Mining and Kwledge iscery in atabases. B Term 2014 Exam 1 Nember 24, 2014 Prf. Carlina Ruiz epartment f Cmputer Science Wrcester Plytechnic Institute NAME: Prf. Ruiz Prblem I: Prblem II: Prblem
More informationSolutions. Definitions pertaining to solutions
Slutis Defiitis pertaiig t slutis Slute is the substace that is disslved. It is usually preset i the smaller amut. Slvet is the substace that des the disslvig. It is usually preset i the larger amut. Slubility
More informationIdentical Particles. We would like to move from the quantum theory of hydrogen to that for the rest of the periodic table
We wuld like t ve fr the quatu thery f hydrge t that fr the rest f the peridic table Oe electr at t ultielectr ats This is cplicated by the iteracti f the electrs with each ther ad by the fact that the
More informationLearning in Gibbsian Fields: How Accurate and How Fast Can It Be?
IEEE TRANSACTIONS ON PATTERN ANAYSIS AND MACHINE INTEIGENCE, VO. 24, NO. 7, JUY 2002 00 earg Gbbsa Fels: Hw Accurate a Hw Fast Ca It Be? Sg Chu Zhu a Xuwe u AbstractÐGbbsa fels r Markv ram fels are wely
More informationHow can standard heats of formation be used to calculate the heat of a reaction?
Answer Key ALE 28. ess s Law and Standard Enthalpies Frmatin (Reerence: Chapter 6 - Silberberg 4 th editin) Imprtant!! Fr answers that invlve a calculatin yu must shw yur wrk neatly using dimensinal analysis
More informationEEO 401 Digital Signal Processing Prof. Mark Fowler
EEO 4 Digital Signal Prcessing Pr. ar Fwler DT Filters te Set #2 Reading Assignment: Sect. 5.4 Prais & anlais /29 Ideal LP Filter Put in the signal we want passed. Suppse that ( ) [, ] X π xn [ ] y[ n]
More informationExercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27%
/A/ mttrt?c,&l6m5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA Exercses, nuts! A cmpany clams that each batch f ttse&n ta-ns 5 2%-cas-hews, 27% almnds, 13% macadama nuts, and 8% brazl nuts. T test ths
More informationMetering Principles & Configurations
Metering Principles & Cnfiguratins CT PT Startc Western Energy Cmpany DCS Presenters Western Energy Cmpany Mnte Wilke Electrical Superintendent Curt Brckel Electrical Supervisr Brady Clbert Electrical
More informationMaximum Likelihood Estimation
Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~
More informationOn the Borda Method for Multicriterial Decision-Making
O the Brda Methd r Multcrteral Decs-Makg Radu A. Pău Iteratal Metary Fud Isttute 700 9 th Street, N.W. Washgt, D.C. 2043 rpau@m.rg r radupau@yah.cm Abstract The preset paper dscusses tw ssues wth multcrteral
More informationHOOKE'S LAW. THE RATE OR SPRING CONSTANT k.
Practces Group Sesso Date Phscs Departmet Mechacs Laborator Studets who made the practce Stamp cotrol Deadle Date HOOKE'S LAW. THE RATE OR SPRING CONSTANT k. IMPORTANT: Iclude uts ad errors all measuremets
More informationCHAPTER Read Chapter 17, sections 1,2,3. End of Chapter problems: 25
CHAPTER 17 1. Read Chapter 17, sectins 1,2,3. End f Chapter prblems: 25 2. Suppse yu are playing a game that uses tw dice. If yu cunt the dts n the dice, yu culd have anywhere frm 2 t 12. The ways f prducing
More informationModel Fitting, RANSAC. Jana Kosecka
Model Fttg, RANSAC Jaa Kosecka Fttg: Issues Prevous strateges Le detecto Hough trasform Smple parametrc model, two parameters m, b m + b Votg strateg Hard to geeralze to hgher dmesos a o + a + a 2 2 +
More informationReproducing kernel Hilbert spaces. Nuno Vasconcelos ECE Department, UCSD
Reprucng ernel Hlbert spaces Nun Vascncels ECE Department UCSD Classfcatn a classfcatn prblem has tw tpes f varables X -vectr f bservatns features n the wrl Y - state class f the wrl Perceptrn: classfer
More informationMATH Midterm Examination Victor Matveev October 26, 2016
MATH 33- Midterm Examiati Victr Matveev Octber 6, 6. (5pts, mi) Suppse f(x) equals si x the iterval < x < (=), ad is a eve peridic extesi f this fucti t the rest f the real lie. Fid the csie series fr
More informationEquilibrium of Stress
Equilibrium f Stress Cnsider tw perpendicular planes passing thrugh a pint p. The stress cmpnents acting n these planes are as shwn in ig. 3.4.1a. These stresses are usuall shwn tgether acting n a small
More informationå 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018
Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of
More informationEquilibrium corporate nance
Equlbrum crprate ace Albert s NYU Per Gttard EUI Gud Ruta NYU ad EUI Octber 2, 2009 Abstract We study a geeral equlbrum mdel wth prduct where acal markets are cmplete. I ths evrmet rms crprate acg decss
More informationCh5 Appendix Q-factor and Smith Chart Matching
Ch5 Appedx -factr ad mth Chart Matchg 5B-1 We-Cha a udwg, F Crcut Deg hery ad Applcat, Chapter 8 -type matchg etwrk w-cmpet Matchg Netwrk hee etwrk ue tw reactve cmpet t trafrm the lad mpedace t the dered
More informationLCAO APPROXIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (cation, anion or radical).
Principles f Organic Chemistry lecture 5, page LCAO APPROIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (catin, anin r radical).. Draw mlecule and set up determinant. 2 3 0 3 C C 2 = 0 C 2 3 0 = -
More informationUnit 2 Expressions, Equations, and Inequalities Math 7
Unit 2 Expressins, Equatins, and Inequalities Math 7 Number f Days: 24 10/23/17 12/1/17 Unit Gals Stage 1 Unit Descriptin: Students cnslidate and expand previus wrk with generating equivalent expressins
More informationBig Data Analytics! Special Topics for Computer Science CSE CSE Mar 31
Bg Data Analytcs! Specal Tpcs fr Cmputer Scence CSE 4095-001 CSE 5095-005! Mar 31 Fe Wang Asscate Prfessr Department f Cmputer Scence and Engneerng fe_wang@ucnn.edu Intrductn t Deep Learnng Perceptrn In
More informationSoftware Process Models there are many process model s in th e li t e ra t u re, s om e a r e prescriptions and some are descriptions you need to mode
Unit 2 : Software Process O b j ec t i ve This unit introduces software systems engineering through a discussion of software processes and their principal characteristics. In order to achieve the desireable
More informationKernel-based Methods and Support Vector Machines
Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg
More informationEC319 Economic Theory and Its Applications, Part II: Lecture 7
EC319 Economic Theory and Its Applications, Part II: Lecture 7 Leonardo Felli NAB.2.14 27 February 2014 Signalling Games Consider the following Bayesian game: Set of players: N = {N, S, }, Nature N strategy
More informationBC Calculus Review Sheet. converges. Use the integral: L 1
BC Clculus Review Sheet Whe yu see the wrds.. Fid the re f the uuded regi represeted y the itegrl (smetimes f ( ) clled hriztl imprper itegrl).. Fid the re f differet uuded regi uder f() frm (,], where
More informationHigher. Specimen NAB Assessment
hsn.uk.net Higher Mathematics UNIT Specimen NAB Assessment HSN50 This dcument was prduced speciall fr the HSN.uk.net website, and we require that an cpies r derivative wrks attribute the wrk t Higher Still
More informationThe Second Anti-Mathima on Game Theory
The Second Ant-Mathma on Game Theory Ath. Kehagas December 1 2006 1 Introducton In ths note we wll examne the noton of game equlbrum for three types of games 1. 2-player 2-acton zero-sum games 2. 2-player
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationLesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method.
Lessn Plan Reach: Ask the students if they ever ppped a bag f micrwave ppcrn and nticed hw many kernels were unppped at the bttm f the bag which made yu wnder if ther brands pp better than the ne yu are
More informationTree Structured Classifier
Tree Structured Classifier Reference: Classificatin and Regressin Trees by L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stne, Chapman & Hall, 98. A Medical Eample (CART): Predict high risk patients
More informationANALYSIS OF A CLASS OF ADAPTIVE ROBUSTIFIED PREDICTORS IN THE PRESENCE OF NOISE UNCERTAINTY
I. K. Kvačevć dr. Aalza jede vrste adaptra rbusg predktra u prsutst epzatg šuma ISS 330-365 Prt, ISS 848-6339 Ole DOI: 0.7559/V-040506383 AALYSIS OF A CLASS OF ADAPIVE ROBUSIFIED PREDICORS I HE PRESECE
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationGENESIS Structural Optimization for ANSYS Mechanical
P3 STRUCTURAL OPTIMIZATION (Vl. II) GENESIS Structural Optimizatin fr ANSYS Mechanical An Integrated Extensin that adds Structural Optimizatin t ANSYS Envirnment New Features and Enhancements Release 2017.03
More informationRelationships Between Frequency, Capacitance, Inductance and Reactance.
P Physics Relatinships between f,, and. Relatinships Between Frequency, apacitance, nductance and Reactance. Purpse: T experimentally verify the relatinships between f, and. The data cllected will lead
More informationANSWER KEY 7 GAME THEORY, ECON 395
ANSWER KEY 7 GAME THEORY, ECON 95 PROFESSOR A. JOSEPH GUSE 1 Gbbos.1 Recall the statc Bertrad duopoly wth homogeeous products: the frms ame prces smultaeously; demad for frm s product s a p f p < p j,
More information+ a a m. y = a 1. x 1. x m. x 2. Basics
Geeral Lear Model Parts of materal: courtes of Tobas Sommer-Blöchl Isttute for Sstems Neuroscece Uverst Medcal Ceter Hamburg-Eppedorf (UKE) Bascs GLM: (multple) regresso = a + b+ Error : depedet varable
More informationComputing Correlated Equilibria in Multi-Player Games
Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,
More information1. Elementary Electronic Circuits with a Diode
ecture 1: truct t electrc aal crcut 361-1-3661 1 1. Elemetary Electrc Crcut wth a e Euee Paer, 2008 HE M OF HE COUSE ume lear tme-arat () electrc crcut t re ay lut t the fllw fe tak (ee F. 1), whch are
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. August 2011
Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly
More informationCS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f + + + K d d K k - parameters
More informationBasics of heteroskedasticity
Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationCambridge Assessment International Education Cambridge Ordinary Level. Published
Cambridge Assessment Internatinal Educatin Cambridge Ordinary Level ADDITIONAL MATHEMATICS 4037/1 Paper 1 Octber/Nvember 017 MARK SCHEME Maximum Mark: 80 Published This mark scheme is published as an aid
More informationLinear regression (cont) Logistic regression
CS 7 Fouatos of Mache Lear Lecture 4 Lear reresso cot Lostc reresso Mlos Hausrecht mlos@cs.ptt.eu 539 Seott Square Lear reresso Vector efto of the moel Iclue bas costat the put vector f - parameters ehts
More informationOn the convergence of derivatives of Bernstein approximation
O the covergece of dervatves of Berste approxmato Mchael S. Floater Abstract: By dfferetatg a remader formula of Stacu, we derve both a error boud ad a asymptotc formula for the dervatves of Berste approxmato.
More informationTrigonometric Functions. Concept Category 3
Trignmetric Functins Cncept Categry 3 Gals 6 basic trig functins (gemetry) Special triangles Inverse trig functins (t find the angles) Unit Circle: Trig identities a b c 2 2 2 The Six Basic Trig functins
More informationRandom Variate Generation ENM 307 SIMULATION. Anadolu Üniversitesi, Endüstri Mühendisliği Bölümü. Yrd. Doç. Dr. Gürkan ÖZTÜRK.
adom Varate Geerato ENM 307 SIMULATION Aadolu Üverstes, Edüstr Mühedslğ Bölümü Yrd. Doç. Dr. Gürka ÖZTÜK 0 adom Varate Geerato adom varate geerato s about procedures for samplg from a varety of wdely-used
More informationWhen a substance heats up (absorbs heat) it is an endothermic reaction with a (+)q
Chemistry Ntes Lecture 15 [st] 3/6/09 IMPORTANT NOTES: -( We finished using the lecture slides frm lecture 14) -In class the challenge prblem was passed ut, it is due Tuesday at :00 P.M. SHARP, :01 is
More information