AI Reference 024 Classical AI & Machine Learning Wikipedia guided Primary sources linked

Bayesian Networks

Definition

A Bayesian network is a directed acyclic graph representing probabilistic relationships between variables using graph structure and conditional probability distributions.

What is a Bayesian network?

A Bayesian network is a directed acyclic graph whose nodes represent variables and whose edges represent conditional dependencies. Each variable has a probability distribution conditioned on its parent nodes.

How inference works

The graph provides a structured way to factor a joint probability distribution. If evidence about some variables is observed, probability rules can update beliefs about others. That makes Bayesian networks useful for diagnosis, prediction and reasoning when information is uncertain.

Why they matter in AI

AI does not operate only with deterministic rules. Bayesian networks became important because they make uncertainty explicit and mathematically tractable. Judea Pearl's work in the 1980s helped establish probabilistic graphical models as a major AI framework.

Related terms, defined

Classical AI & Machine Learning Fuzzy Logic Fuzzy logic allows degrees of membership between 0 and 1 rather than forcing every proposition into a strict true-or-false category. Classical AI & Machine Learning Genetic Algorithms Genetic algorithms are optimization methods inspired by evolutionary selection, iteratively varying and selecting candidate solutions according to a fitness function. Classical AI & Machine Learning Reinforcement Learning Reinforcement learning trains an agent by interaction: actions change an environment, rewards provide feedback and the agent learns a policy intended to maximize cumulative reward. Classical AI & Machine Learning Supervised Learning Supervised learning trains a model from examples paired with known labels or target values. Classification and regression are common supervised tasks. Classical AI & Machine Learning Unsupervised Learning Supervised learning trains a model from examples paired with known labels or target values. Classification and regression are common supervised tasks.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

← The AI Winters Fuzzy Logic →