Class on 1/26 will be on zoom (meeting ID: 946 8858 7326)
Tentative schedule below:
- Lecture 0: Introduction & review on Bayesian basics.
- Lecture 1: Introduction to Gaussian process. [Data: CanadianWages]
- Lecture 2: Latent Gaussian process.
- Lecture 3: Dirichlet Process and Dirichlet Process Mixtures.
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Lecture 4: Consistency and Contraction rates.
- Lecture 5: Scalable BNP: A Case Study.
- Lecture 6: Basics of MCMC
- Lecture 7: Importance Sampling.
- Lecture 8: Distributional Approximation, EM algorithm, Variational Bayes.
- Lecture 9: Trees (CART, bagging, random forests, Bayesian CART, BART)
- Lecture 10: Symbolic regression: bridging nonparametrics and parametrics