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A Bayesian network is a type of graphical model that uses probability to determine the occurrence of an event. It is also known as a belief network or a causal network. It consists of directed cyclic graphs (DCGs) and a table of conditional probabilities to find out the probability of an event happening.
A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. In other words, it's a way to model complex systems or processes by breaking them down into smaller parts and representing the relationships between those parts in a graphical format.
Bayesian networks are used in artificial intelligence as a tool for decision-making under uncertainty. They allow for the modeling of uncertainty in a probabilistic way and enable reasoning and inference about the likelihood of events or outcomes based on observed evidence or data.
In essence, Bayesian networks provide a formal framework for reasoning and decision-making under uncertainty, which is a key aspect of many artificial intelligence applications, such as natural language processing, computer vision, and robotics.
Bayesian networks are used to model complex systems and make predictions based on available data and the relationships between variables. These networks use conditional probability distributions to describe the relationships between variables and calculate the probability of an event given related events. They are used in a variety of applications, such as medical diagnosis, speech recognition, and image processing. Additionally, Bayesian networks are useful in artificial intelligence because they enable decision-making under uncertainty and can help build intelligent systems that learn from data and make decisions based on that learning.
Saad Hassouna Hamdi Al-Jamimi
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