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Dynamic Bayesian Networks - Fundamentals and Applications - cover

Dynamic Bayesian Networks - Fundamentals and Applications

Fouad Sabry

Publisher: One Billion Knowledgeable

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Summary

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.
Available since: 07/01/2023.
Print length: 99 pages.

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