Bayesian vs. Markov Networks: An Intuitive Guide to Probabilistic Graphical Models
By
Sean Moran
Summary
An intuitive introduction to probabilistic graphical models, explaining how Bayesian networks (directed) and Markov networks (undirected) represent structured uncertainty. The article covers the fundamentals of reasoning under uncertainty, from prediction problems to the mathematical frameworks behind directed and undirected graphical models, including weighted logical rules. It bridges the gap between abstract probabilistic concepts and practical applications like churn prediction, fraud detection, and document classification.
Source
bskyBayesian vs. Markov Networks: An Intuitive Guide to Probabilistic Graphical Modelstowardsdatascience.comKey quotes
· 3 pulledA churn model estimates whether a customer is likely to leave. A fraud model estimates whether a transaction is suspicious.
In each case, the setup is broadly the same. We have some observed information, usually called the inputs or features, and a thing we want to predict, usually called the target.
An intuitive introduction to reasoning with uncertainty, from directed Bayesian networks to undirected Markov networks and weighted logical rules.
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