# STAT4010 Bayesian Learning (2023 Spring)

## Tutorial Information

Time: Every Thursday 16:30-17:15

Venue: LSB LT2

Tutorial | Topics | Notes | Codes | Remark | |
---|---|---|---|---|---|

2-Feb-2023 | 01 | Bayesian Philosophy, Posterior Calculation, R Review | Note 01 | tut1.R | |

9-Feb-2023 | 02 | Representation, Invariant Priors | Note 02 | tut2.R | |

16-Feb-2023 | 03 | Prior Design | Note 03 | tut3.R | |

23-Feb-2023 | 04 | Loss, Risk, and Bayes Estimator | Note 04 | tut4.R | |

2-Mar-2023 | \ | \ | Consultation Session | ||

9-Mar-2023 | \ | \ | Consultation Session | ||

16-Mar-2023 | 05 | Adimissibility, Minimax Estimator | Note 05 | tut5.R | |

23-Mar-2023 | 06 | Bayesian Test, Bayes Factor | Note 06 | tut6.R | |

30-Mar-2023 | 07 | Region Estimation, Coding Problems, Theoretical Justification | Note 07 | tut7.R | |

6-Apr-2023 | 08 | Trapezoidal Rule, Inverse Transform Sampling, Importance Sampling | Note 08 | tut8.R | |

13-Apr-2023 | 09 | Reweighting Importance Sampling, MH algorithm | Note 09 | tut9.R | |

20-Apr-2023 | 10 | Gibbs Sampler, MH within Gibbs | Note 10 | tut10.R |

## References

- Course Website (This is your major reference for revision.)
- E-learning Platform (You may want to check the platform before bringing questions to us.)