Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan

Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan

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Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan

In the field of artificial intelligence, expert systems play a crucial role in making decisions and solving complex problems. One key aspect of expert systems is probabilistic reasoning, which allows for uncertainty to be incorporated into the decision-making process.

In his book “Probabilistic Reasoning in Expert Systems: Theory and Algorithms,” Judea Pearl provides a comprehensive overview of the theoretical foundations and practical algorithms for incorporating probabilistic reasoning into expert systems. This book is a must-read for anyone interested in understanding how uncertainty can be effectively managed in decision-making processes.

Neapolitan’s book delves into the mathematical principles behind probabilistic reasoning, such as Bayesian networks and Markov models, and provides practical examples and algorithms for implementing these concepts in expert systems. By understanding the theory and algorithms presented in this book, practitioners can enhance the capabilities of their expert systems and make more informed decisions in uncertain environments.

Overall, “Probabilistic Reasoning in Expert Systems: Theory and Algorithms” is a valuable resource for researchers, practitioners, and students looking to deepen their understanding of probabilistic reasoning and its applications in expert systems. With its clear explanations and practical examples, this book is sure to be a valuable addition to any AI enthusiast’s library.
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