Practical Applications of Bayesian Networks

November 8, 2018 @ 6:00 pm – 8:00 pm
Inc. 3165 Kifer Road Building-B Cafeteria Santa Clara

by Reza Azarkhail

Bayesian Networks are webs of interconnected random variables through conditional probability distributions. They are widely used for uncertainty and complexity management in most modern modeling approaches including: machine learning, artificial intelligence, and patterns recognition. They are intuitive for complexity management, because they are modular, so the complexity can be modeled one piece at a time. They are also perfect for uncertainty management, because they are probabilistic in nature. The probability theory connects the parts together and ensures the overall system consistency, also provides a means to connect the models to external data when available. This talk is a brief introduction to Bayesian Networks with few examples in different application

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