{"id":158842,"date":"2024-01-29T15:34:35","date_gmt":"2024-01-29T15:34:35","guid":{"rendered":"https:\/\/www.techopedia.com\/?post_type=definition&p=158842"},"modified":"2024-01-29T15:34:35","modified_gmt":"2024-01-29T15:34:35","slug":"bayesian-network","status":"publish","type":"definition","link":"https:\/\/www.techopedia.com\/definition\/bayesian-network","title":{"rendered":"Bayesian Network"},"content":{"rendered":"

What is a Bayesian Network?<\/span><\/h2>\n

A Bayesian Network is a <\/span>statistical model<\/span><\/a> that represents a set of variables and their probabilistic relationships.\u00a0<\/span><\/p>\n

Imagine it as a web of interconnected nodes, where each node symbolizes a variable, and the links between them represent the probabilistic dependencies.<\/span><\/p>\n

These networks are named after <\/span>Thomas Bayes<\/span><\/a>, an 18th-century mathematician, who laid the foundation for <\/span>Bayesian inference<\/span><\/a>.<\/span><\/p>\n

Techopedia Explains<\/span><\/h3>\n

The development of Bayesian Networks can be traced back to the late 20th century when they emerged as a fusion of <\/span>graph theory<\/span><\/a> and <\/span>probability theory<\/span><\/a>. They were primarily developed to handle uncertainty in complex systems, a challenge often encountered in fields like <\/span>artificial intelligence<\/span><\/a> (AI) and decision-making processes.<\/span><\/p>\n

The core meaning of a Bayesian Network is rooted in a few basic principles:<\/span><\/p>\n

    \n
  1. Conditional Probability<\/b>: This is the likelihood of an event occurring given the occurrence of another event. Bayesian Networks use this principle to infer the probability of unknown variables based on known variables.<\/span><\/li>\n
  2. Directed Acyclic Graph (DAG)<\/b>: <\/span>This is a directional graph<\/span><\/a> with nodes (representing variables) and edges (representing dependencies) that do not contain any loops. Each edge in this graph directs from a cause to its effect.<\/span><\/li>\n
  3. Inference<\/b>: <\/span>Using the network<\/span><\/a>, one can make predictions or decisions by calculating the probabilities of certain outcomes.<\/span><\/li>\n<\/ol>\n

    By integrating these principles, Bayesian Networks provide a framework for modeling complex systems and predicting outcomes. This, in turn, makes them an important tool in modern <\/span>data analysis<\/span><\/a> and AI.<\/span><\/p>\n

    Key Components of a Bayesian Network<\/span><\/h2>\n

    \"Key<\/p>\n

    Bayesian Networks comprise several elements that work together to create a comprehensive model for analyzing and predicting probabilities.<\/span><\/p>\n

    Nodes and Edges<\/b><\/p>\n