Courses

• Stats 201C Advanced Modeling and Inference
• Stats 212 Graphical Models
• Stats C180/236 Introduction to Bayesian Statistics
• Stats 210 Computer Intensive Methods
• Stats M254 Statistical Methods in Computational Biology
• Stats 102B Computation and Optimization for Statistics
• Stats 102C Introduction to Monte Carlo Methods

Lecture Notes

• Advanced Modeling and Inference:
  1. Incomplete data and the EM algorithm.
  2. Bayesian inference with missing data.
  3. Mixture modeling.
  4. Hidden Markov models.
  5. Random graphs for modeling network data.
  6. Introduction to graphical models.
  7. Causal DAGs: inference and learning.
• Graphical Models:
  1. Conditional independence.
  2. Undirected graphical models.
  3. Directed acyclic graphs.
  4. DAG-based causal inference.
  5. Structure learning of DAGs.
  6. Directed mixed graphs for latent variables.
• Introduction to Monte Carlo Methods:
  1. Introduction, examples, and references.
  2. Importance sampling and sequential Monte Carlo.
  3. Markov chains.
  4. Markov chain Monte Carlo.
  5. The Gibbs sampler and applications.