| Tuesday, January 12 |
Course logistics
What is Bayesian Analysis?
Bayes estimator: a first example
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| Thursday, January 14 |
Project guidelines
Bayes estimator: a first example
Point estimates, confidence estimates, and the Bayes estimator
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| Tuesday, January 19 |
Why Bayes?
Directed graphical models
Regression
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Readings on decision theoretic foundations
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| Thursday, January 21 |
Regression
Hierarchical models
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Exercises on Bayes estimators
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| Tuesday, January 26 |
Hierarchical models
Basics of model selection
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Readings on graphical models
Readings on PPLs
Optional readings on common distributions
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| Thursday, January 28 |
Calibration
Consistency, misspecification, identifiability
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| Friday, January 29 |
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Exercises on regression
Exercises on hierarchical modelling
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| Tuesday, February 2 |
Calibration
Exchangeability and de Finetti
Q&A
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Optional PT readings
Readings on prior distributions
Readings on model selection
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| Thursday, February 4 |
Calibration, continued; Bernstein-von Mises
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Short exercise (before class)
Optional modelling exercise
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| Tuesday, February 9 |
Bayes estimators: properties and optimization
Mixtures
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Readings on goodness-of-fit
Optional readings on exchangeability
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| Thursday, February 11 |
Metropolis-Hastings and slice samplers
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Project proposals
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| Tuesday, February 16 |
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No lecture (reading week)
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| Thursday, February 18 |
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No lecture (reading week)
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| Tuesday, February 23 |
Design of MCMC samplers
Parallel tempering
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Readings on Bayesian clustering
Optional readings on slice sampling
Optional readings on evaluation of MCMC methods
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| Thursday, February 25 |
Approximate Bayesian Computation
Normalization constant estimation via stepping stone
Normalization constant estimation via reversible jumps
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Markov chain and linear algebra exercise
Global vs. detail balance exercise
Optional exercise on the law of large numbers
Exercise on MCMC on uniforms
Exercises on Metropolis-Hastings
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| Tuesday, March 2 |
MH and slice exercises solutions
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Optional MCMC readings
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| Friday, April 23 |
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Final project
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