This episode is a comprehensive guide to understanding Bayesian networks, which are powerful tools for modeling probabilistic relationships between variables. Our sources:https://www.geeksforgeeks.org/artificial-intelligence/understanding-bayesian-networks-modeling-probabilistic-relationships-between-variables/https://link.springer.com/article/10.1007/s10489-025-06289-5https://www.mdpi.com/2079-8954/13/2/131Bayesian networks (BNs), also known as belief networks or Bayesian belief networks (BBNs), are powerful probabilistic graphical models specifically designed to represent and reason about uncertain knowledge by encoding probabilistic relationships among variables.The Geeksforgeeks article explains the structure of BNs, consisting of nodes and directed edges, and the concept of conditional independence that simplifies complex probabilistic relationships. It details how these networks define a joint probability distribution and discusses methods for inference, which involves calculating probabilities from the network, and learning, which focuses on determining the network's structure and parameters from data. The text also provides a structured answer to a common interview question regarding Bayesian networks, highlighting their applications in fields like artificial intelligence, bioinformatics, and decision analysis. Hosted on Acast. See acast.com/privacy for more information.

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