- Lecture 0: Introduction & review on Bayesian basics.
- Lecture 1: Introduction to Gaussian process. [Data: CanadianWages] [Homework1](Homework 1 Due 9/13 on Wed by 11:59pm)
- Lecture 2: Latent Gaussian process.
- Lecture 3: Dirichlet Process and Dirichlet Process Mixtures. [Tutorial: Finite Mixture Model] [Rousseau and Mengerson (JRSSB, 2011)][Blocked Gibbs]
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Lecture 4: Consistency and Contraction rates.
- Lecture 5: Scalable BNP: A Case Study. [Efficient GP_Banerjee et al. _13_Biometrika]
- Lecture 6: Basics of MCMC [Rcode]
- Lecture 7: Importance Sampling. [Sequential MC][Particle filtering]
- Lecture 8: Distributional Approximation, EM algorithm, Variational Bayes. [INLA][INLA Review][Convergence rate of EM][Varitional Inference review]
- Lecture 9: Trees (CART, bagging, random forests, Bayesian CART, BART)
- Structured ensembling: [Diebold, Shin – 2018] [Double spike Dirichlet]
- Symbolic regression: bridging nonparametrics and parametrics [Symbolic Regression]