6.882 Bayesian Modeling and Inference (Spring 2016)

Room 4-153
Tuesday, Thursday 2:30–4:00 PM

  Professor Tamara Broderick
  Office Hours: Tuesday & Thursday, 4–5pm, 32-G498


As both the number of data sets and data set sizes grow, practitioners are interested in learning increasingly complex information and interactions from data. Probabilistic modeling in general, and Bayesian approaches in particular, provide a unifying framework for flexible modeling that includes prediction, estimation, and coherent uncertainty quantification. In this course, we will cover the modern challenges of Bayesian inference, including (but not limited to) speed of approximate inference, making use of distributed architectures, streaming data, and complex data interactions. We will study Bayesian nonparametric models, wherein model complexity grows with the size of the data; this allows us to learn, e.g., a greater diversity of topics as we read more documents from Wikipedia, identify more friend groups as we process more of Facebook's network structure, etc.

Piazza Site

Scribed notes, readings, discussions outside of class, and other resources can be found at the course Piazza page. (Email Prof. Broderick if you are having trouble accessing Piazza.)


This course will cover Bayesian modeling and inference at an advanced graduate level. A tentative list of topics (which may change depending on our interests) is as follows:


A graduate-level familiarity with statistics, machine learning, and probability is required. We will assume familiarity with graphical models, exponential families, finite-dimensional Gaussian mixture models, expectation maximization, linear & logistic regression, hidden Markov models.

Some good courses to have taken beforehand include: 6.437, 6.438, 6.867, 6.260 (theoretical stats)