Note: I will review all these topics at the start of this course, this will help in case you have forgotten things. If you have never seen this review material before, it will be hard going.

The current version of the lecture notes is available here. Please note that I will be adding a chapter or two towards the end of this course. The homework assignments are in the lecture notes. I will spend one week on each topic id, except maybe Gibbs sampling and Metropolis-Hastings, which might take two weeks.

The schedule is as follows. ó

Topic id | Content | Data for class | Homework |
---|---|---|---|

1 | Introduction and review | ch1 data | set up JAGS and test it |

2 | Probability theory and probability distributions | HW 1 | |

3 | Important distributions | work through chapter | |

4 | Jointly distributed random variables | work through chapter | |

5 | Maximum Likelihood Estimation | play with optim etc. | |

6 | Basics of bayesian statistics | HW 2, 3, 4, 5 | |

7 | Gibbs sampling and the Metropolis-Hastings algorithm | HW8 | |

8 | Using JAGS for modeling | HW 6 | |

9 | Priors | HW 7, 8 | |

10 | Regression models | HW 9, 10 | |

11 | Linear mixed models | HW 11, 12 |

The data are here.

All JAGS and Stan code is here.