Fall 2019 – IDS.190 – Topics in Bayesian Modeling and Computation

Slides related to this course are available for MIT students and faculty here.

Sept 11 – Automated Data Summarization for Scalability in Bayesian Inference
Tamara Broderick, MIT

September 18 – Probabilistic Modeling meets Deep Learning using TensorFlow
Brian Patton, Google AI

October 2 – Behavior of the Gibbs Sampler in the Imbalanced Case and Bias
Correction from Daily Min and Max Temperature Measurements
Natesh Pillai, Harvard

October 9 – Probabilistic Programming and Artificial Intelligence
Vikash Mansinghka, MIT

October 16 – Markov Chain Monte Carlo Methods and Some Attempts at
Parallelizing Them
Pierre E. Jacob, Harvard University

October 23 – Esther Williams in the Harold Holt Memorial Swimming Pool: Some
Thoughts on Complexity
Daniel Simpson, University of Toronto

October 30 – Using Bagged Posteriors for Robust Inference
Jonathan Huggins, Boston University

November 6 – Probabilistic Inference and Learning with Stein’s Method
Lester Mackey, Microsoft Research

November 13 – Artificial Bayesian Monte Carlo Integration: A Practical Resolution
to the Bayesian (Normalizing Constant) Paradox
Xiao-Li Meng, Harvard University

November 20 – A Causal Exposure Response Function with Local Adjustment for
Confounding: A study of the health effects of long-term exposure to low levels of
fine particulate matter
Francesca Dominici, Harvard University

December 4 – Flexible Perturbation Models for Robustness to Misspecification
Jeffrey Miller, Harvard University

December 11 – The Statistical Finite Element Method
Mark Girolami, University of Cambridge




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