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What Is Bayesian Decision Networks
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: Influence diagram
Chapter 3: Graphical model
Chapter 4: Hidden Markov model
Chapter 5: Decision tree
Chapter 6: Gibbs sampling
Chapter 7: Decision analysis
Chapter 8: Value of information
Chapter 9: Probabilistic forecasting
Chapter 10: Causal graph
(II) Answering the public top questions about bayesian decision networks.
(III) Real world examples for the usage of bayesian decision networks in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian decision networks' technologies.
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 decision networks.
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: Influence diagram
Chapter 3: Graphical model
Chapter 4: Hidden Markov model
Chapter 5: Decision tree
Chapter 6: Gibbs sampling
Chapter 7: Decision analysis
Chapter 8: Value of information
Chapter 9: Probabilistic forecasting
Chapter 10: Causal graph
(II) Answering the public top questions about bayesian decision networks.
(III) Real world examples for the usage of bayesian decision networks in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian decision networks' technologies.
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 decision networks.