Models, Inference and Algorithms
Broad Institute of MIT and Harvard
September 21, 2016
Alp Kucukelbir
Columbia CS
MIA Meeting: https://youtu.be/kfptzBEICEs?t=2984
Automated Inference and the Promise of Probabilistic Programming
Abstract: Generative probability models allow us to 1) express assumptions about hidden patterns in data, 2) infer such hidden patterns, and 3) evaluate the accuracy of our findings. However, designing modern models, developing custom inference algorithms, and evaluating accuracy requires enormous effort and cross-disciplinary expertise. Probabilistic programming promises to enable this process by making each step less arduous and more automated. I will begin describing how probabilistic programming can help design modern probability models. I will then focus on automating inference for a wide class of probability models. To this end, I will describe automatic differentiation variational inference, a fully automated approximate inference algorithm. I will demonstrate its application to a mixture modeling analysis of a dataset with millions of observations. I intend to conclude with some thoughts on model evaluation, with a population genetics example. Throughout this talk, I will highlight connections to our software project, Edward: a Python library for probabilistic modeling, inference, and evaluation.
MIA Primer:
Rajesh Ranganath
Princeton CS
Primer: Probabilistic Generative Models and Posterior Inference
To model data we desire to express assumptions about the data, infer hidden structure, make predictions, and simulate new data. In this talk, I will describe how probabilistic generative models provide a common toolkit to meet these challenges. I will first present these ideas in a toy setting followed by discussing the range of probabilistic generative models from structural to algorithmic. Next I will present an in depth view of deep exponential families, a class of probability models containing both predictive and interpretive models. I will end with the central computational problem in realizing the promise of probabilistic generative models: posterior inference. I will demonstrate why deriving inference is tedious and will touch on black box variational methods which seek to alleviate this burden.
For more information visit: https://www.broadinstitute.org/mia
Copyright Broad Institute, 2016. All rights reserved.
