Bayesian Network: Fundamentals and Applications

A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional relationships via a directed acyclic graph (DAG). In other words, a Bayesian network is a type of directed acyclic graph. Bayesian networks are perfect for determining the likelihood that any one of multiple possible known causes was the contributing factor in an event that has already taken place and making a prediction based on that likelihood. For instance, the probabilistic links that exist between diseases and symptoms might be represented by a Bayesian network. The network may be used to compute the odds of the presence of a variety of diseases based on the symptoms that are provided.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Bayesian Network

Chapter 2: Likelihood Function

Chapter 3: Bayesian Inference

Chapter 4: Posterior Probability

Chapter 5: Graphical Model

Chapter 6: Expectation-Maximization Algorithm

Chapter 7: Gibbs Sampling

Chapter 8: Markov Random Field

Chapter 9: Maximum a Posteriori Estimation

Chapter 10: Bayesian Hierarchical Modeling

(II) Answering the public top questions about bayesian network.

(III) Real world examples for the usage of bayesian network in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian network.

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