https://lc.kubagro.ru/Байесовские_модели_восприятия_и_действия_2023.pdf
Bayesian networks are a powerful tool in AI for reasoning under uncertainty, decision making, and knowledge representation, enabling more robust and intelligent systems.
I hope this attached link will explain to you https://www.cs.toronto.edu/~duvenaud/distill_bayes_net/public/
A Bayesian network is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. They are used in probabilistic reasoning, inference, and prediction. They are particularly useful in analyzing complex problems involving uncertainty and incomplete information. By representing the relationships between variables in a Bayesian network, it allows for more efficient and accurate reasoning about the underlying system. they are widely used in various fields such as artificial intelligence, machine learning, genetics, medicine, and finance. They are also commonly used in decision support systems, risk assessment, and predictive modeling.
A Bayesian network is a model that uses probability to represent how different variables influence each other, helping AI to reason, make decisions, and learn under uncertainty. It's key for AI's ability to handle real-world complexities.