Wiki source code of Study Group, spring 2012

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1 = Bayesian core: a practical approach to computational Bayesian statistics (study group) =
2
3 === Scope ===
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5 4 weekly exercises (1 for everybody plus 3 exercise of your own choice) are needed to collect credits.
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7 === Type ===
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9 Advanced studies
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11 === Prerequisites ===
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13 (% style="color: rgb(0,51,0);" %)The minimal prerequisites for this course are a mastering of basic probability theory for discrete and continuous variables and of basic statistics (MLE, sufficient statistics).
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15 === Lectures ===
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17 Room C131 on Thursdays 10-12 during period IV (on May 3 in room C130).
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19 === Content scheme ===
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21 The purpose of this book is to provide a self-contained entry to practical & computational Bayesian statistics using generic examples from the most common models. The emphasis on practice is a strong feature of this book in that its primary audience is made of graduate students that need to use (Bayesian) statistics as a tool to analyze their experiments and/or data sets. The book should also appeal to scientists in all fields, given the versatility of the Bayesian tools. It can also be used for a more classical statistics audience when aiming at teaching a quick entry to Bayesian statistics at the end of an undergraduate program for instance.
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23 The chapters of the book and their topics are:
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25 1. **Normal models**
26 1*. Conditional distributions, priors, posteriors, improper priors, conjugate priors, exponential families, tests, Bayes factors, decision theory, importance sampling
27 1. **Regression and variable selection**
28 1*. G-priors, noninformative priors, Gibbs sampling, variable selection
29 1. **Generalised linear models**
30 1*. Probit, logit and log-linear models, Metropolis Hastings algorithms, model choice
31 1. **Capture~-~-recapture experiments**
32 1*. Sampling models, open populations, accept reject algorithm, Arnason Schwarz model
33 1. **Mixture models**
34 1*. Completion, variable dimensional models, label switching, tempering, reversible jump MCMC
35 1. **Dynamic models**
36 1*. AR, MA and ARMA models, state-space representation, hidden Markov models, forward-backward algorithm
37 1. **Image analysis**
38 1*. k-nearest-neighbor, supervised classification, segmentation, Markov random fields, Potts model
39
40 ----
41
42 * Readings (and required exercise for everyone):
43 * Week 1 (31.01.2012) : Chapter 2
44 * Week 2 (07.02.2012) : - (exercise 2.22)
45 * Week 3 (14.02.2012) : Chapter 3 up to 3.3 (exercise 3.4)
46 * Week 4 (21.02.2012) : Chapter 3 from 3.3 to end (exercise 3.13)
47 * Week 5 (28.02.2012) : No meeting because of exams
48 * Week 6 (06.03.2012) : No meeting because of period break
49 * Week 7 (15.03.2012) : All of Chapter 4 (exercise 4.4)
50 * Week 8 (22.03.2012) : All of Chapter 5 (exercise 5.5)
51 * Week 9 (29.03.2012) : Chapter 6 up to 6.6 (exercise 6.1)
52 * Week 10 (05.04.2012) : No meeting because of easter break
53 * Week 11 (12.04.2012) : Chapter 6 from 6.6 to end (exercise 6.17)
54 * Week 12 (19.04.2012) : Chapter 7 up to 7.2.2 (exercise 7.9)
55 * Week 13 (26.04.2012) : Chapter 8 up to 8.3
56
57 === Bibliography ===
58
59 * Marin J, Robert C. [[Bayesian core: a practical approach to computational Bayesian statistics.>>url:http://www.ceremade.dauphine.fr/~~xian/BCS/||shape="rect"]] New York: Springer; 2007.
60 * Banerjee S. Bayesian linear model: Gory details 1 the NIG conjugate prior family [document on the internet]. Minneapolis (MN): University of Minnesota; 2008 [cited 2012 Feb 16]. Available from: [[http:~~/~~/www.biostat.umn.edu/~~~~ph7440/pubh7440/BayesianLinearModelGoryDetails.pdf>>url:http://www.biostat.umn.edu/~~ph7440/pubh7440/BayesianLinearModelGoryDetails.pdf||shape="rect"]].