Probabilistic Inference using Bayesian Networks

In this work, we study the application of bayesian networks for probabilistic inference. We consider a hypothetical real-world scenario where we answer queries regarding various events (health problems, accidents etc.) caused by factors such as air pollution, bad road conditions etc.

Each event/factor is modeled as a random variable with a certain probability distribution function (given as input). Variable dependence graph is constructed and bayes rule is applied on the markov blanket of the query variables to reduce the computational effort. Detailed documentation can be found in the code.

The code is publicly available at github.