Bayesian Network

Naive Bayes Model

What is Bayesian Network?

Bayesian Networks(BN) build on the same intuitions as the naive Bayes model by exploiting conditional independence properties of the distribution in order to allow a compact and natural representation. They allow us the flexibility to tailor our representation of the distribution to the independence properties that appear reasonable in the current setting. The core of the Bayesian network representation is a directed acyclic graph(DAG), whose nodes are the random variables in our domain and whose edges correspond, intuitively, to direct influence of one node on another.

This graph can be viewed in two very different ways

as a data structure that provides the skeleton for representing a joint distribution compactly in a factorized way

local probability model

reasoning patterns

as a compact representation for a set of conditional independence assumptions about a distribution

Independence in student example

Bayesian Network Semantics

conclusion