Intermediate Bayesian Statistical Modeling
Calendar and Notes
Calendar And Notes
| WEEK | DAY | TOPIC | NOTES |
| WEEK 1 | Tues. 04/03 | Introduction. Description of the syllabus. Background material | slides1.pdf |
| Thus. 04/05 | Background material | slides2.pdf | |
| WEEK 2 | Tues. 04/10 | Large sample inference Chp. 4, Chp. 10, 13.3 |
slides3.pdf |
| Thus. 04/12 | The multinomial and the multivariate normal models. 3.5,3.6 |
slides4.pdf | |
| WEEK 3 | Tues. 04/17 | Hierarchical models and meta-analysis. 5.1-5.6 |
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| Thus. 04/19 | Model Checking. 6.1-6.5 |
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| WEEK 4 | Tues. 04/24 | Model comparison. 7.1-7.4 Quiz 1 (25%) |
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| Thus. 04/26 | Accounting for data collection schemes. 8.1-8.5 |
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| WEEK 5 | Tues. 05/01 | Observational studies. Censoring and truncation. 8.6-8.8 |
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| Thus. 05/03 | Auxiliary variables for Monte Carlo methods. 12.1 |
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| WEEK 6 | Tues. 05/08 | Regression models. 14.1-14.8 |
slides10.pdf |
| Thus. 05/10 | Regression models. 14.1-14.8 |
slides11.pdf | |
| WEEK 7 | Tues. 05/15 |
Midterm (45%) |
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| Thus. 05/17 | G-priors. Regularization. Robust Inference. 17.1-17.5 |
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| WEEK 8 | Tues. 05/22 | Mixture models. 22.1-22.5 |
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| Thus. 05/24 | Mixture models. 22.1-22.5 |
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| WEEK 9 | Tues. 05/29 | Posterior Modes. EM algorithm. 13.1-13.4 |
slides12.pdf |
| Thus. 05/31 | Efficient Gibbs and Metropolis samplers. 12.1-12.3 |
slides13.pdf | |
| WEEK 10 | Tues. 06/05 |
Approximations |
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| Thus. 06/07 |
Gaussian process models |