531f10INTRO

531f10INTRO - STAT 531: Monte Carlo Method & Statistical...

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Statistical Computing HM Kim Department of Mathematics and Statistics University of Calgary Fall 2010 1/18
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Course Details lectures: Tuesday and Thursday, 11–12:15, MS 427 lab: Tuesday, 9:00 – 9:50 am, MS 571 office: MS 442, (403) 220–5691 email: [email protected] office hours: by appointment Fall 2010 2/18
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Textbook, Important Dates, etc. references: Introducing Monte Carlo Methods with R Elements of statistical computing by Ronald A. Thisted Computational Statistics by Geof H. Givens and Jennifer A. Hoeting announcements, lectures, etc. will be posted on Blackboard r grading: assignments [4] 80% midterm [1] 20% Fall 2010 3/18
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Topics 1 Introduction ( R ) 2 Monte Carlo Simulation 3 Random Variable Generation 4 Monte Carlo Integration 5 Newton-Raphson Method 6 EM algorithm 7 Markov Chains 8 Metropolis-Hastings (MH) Algorithm 9 Gibbs Sampler 10 Bayesian Method Fall 2010 4/18
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Calculus function, f ( x ) graph of a function maxima and minima of continuous functions derivative function, f ( x ) x = f 0 ( x ) integration (4) Statistics random variable (3) probability distribution parameter vs. statistic parameter estimation (5, 6, 8, 9,10) limiting distribution (7) Bayesian vs. frequentist (8,9,10) Fall 2010 5/18
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This note was uploaded on 02/04/2011 for the course STAT 531 taught by Professor Gaborlukacs during the Spring '11 term at Manitoba.

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531f10INTRO - STAT 531: Monte Carlo Method & Statistical...

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