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What Is Dynamic Bayesian Networks
A Bayesian network (BN) is referred to as a Dynamic Bayesian Network (DBN), which is a network that ties variables to each other throughout consecutive time steps.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Dynamic Bayesian Network
Chapter 2: Bayesian Network
Chapter 3: Hidden Markov Model
Chapter 4: Graphical Model
Chapter 5: Recursive Bayesian Estimation
Chapter 6: Time Series
Chapter 7: Statistical Relational Learning
Chapter 8: Bayesian Programming
Chapter 9: Switching Kalman Filter
Chapter 10: Dependency Network (Graphical Model)
(II) Answering the public top questions about dynamic bayesian networks.
(III) Real world examples for the usage of dynamic bayesian networks in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of dynamic bayesian 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 dynamic bayesian networks.
A Bayesian network (BN) is referred to as a Dynamic Bayesian Network (DBN), which is a network that ties variables to each other throughout consecutive time steps.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Dynamic Bayesian Network
Chapter 2: Bayesian Network
Chapter 3: Hidden Markov Model
Chapter 4: Graphical Model
Chapter 5: Recursive Bayesian Estimation
Chapter 6: Time Series
Chapter 7: Statistical Relational Learning
Chapter 8: Bayesian Programming
Chapter 9: Switching Kalman Filter
Chapter 10: Dependency Network (Graphical Model)
(II) Answering the public top questions about dynamic bayesian networks.
(III) Real world examples for the usage of dynamic bayesian networks in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of dynamic bayesian 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 dynamic bayesian networks.