Formally, Bayesian networks are directed acyclic graphs (DAGs) whose nodes represent variables in the Bayesian sense: they may be observable … 展开

Let us use an illustration to enforce the concepts of a Bayesian network. Suppose we want to model the dependencies between three variables: the sprinkler (or more appropriately, its … 展开

Given data $${\displaystyle x\,\!}$$ and parameter $${\displaystyle \theta }$$, a simple Bayesian analysis starts with a prior probability 展开

In 1990, while working at Stanford University on large bioinformatic applications, Cooper proved that exact inference in Bayesian networks is NP-hard. This result prompted … 展开

Bayesian networks perform three main inference tasks:
Inferring unobserved variables
Because a Bayesian network is a complete model for its … 展开

Several equivalent definitions of a Bayesian network have been offered. For the following, let G = (V,E) be a directed acyclic graph (DAG) … 展开

Notable software for Bayesian networks include:
• Just another Gibbs sampler (JAGS) – Open-source alternative to WinBUGS. Uses Gibbs sampling.
• OpenBUGS – Open-source development of WinBUGS. 展开