Introduction to Monte Carlo Methods for Probabilistic Inference

Speaker:  Indrajit Bhattacharya – Kolkata, India
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Abstract

Probabilistic Models provide a framework for representing and reasoning with complex domains with many uncertain and interdependent variables and form one of the foundations of Artificial Intelligence. Unfortunately, the price for elegant representation in such models is hardness of the inference problem. Performing exact inference is computationally intractable for all but the simplest and smallest probabilistic models. One of the principled ways for dealing with this hardness is using sampling techniques. In this talk, I will introduce the Monte Carlo principle and its motivation for probabilistic inference. I will then cover specific Monte Carlo techniques such as rejection sampling, importance sampling and Markov Chain Monte Carlo techniques such as Metropolis Hastings and Gibbs Sampling.  

About this Lecture

Number of Slides:  60
Duration:  60 - 90 minutes
Languages Available:  English
Last Updated: 

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