Hidden Markov Model: Fundamentals and Applications

· One Billion Knowledgeable · AI 讲述者:Mason(来自 Google)
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What Is Hidden Markov Model


A hidden Markov model, often known as an HMM, is a type of statistical Markov model. In an HMM, the system being represented is considered to be a Markov process, which we will refer to as it, with states that cannot be observed (thus the name "hidden"). In order to fulfill one of the requirements for the definition of HMM, there must be a measurable process whose results are "influenced" by those of another process in a certain way. Since it is not possible to directly see, the objective here is to learn about via observing. HMM contains the additional criterion that the result of an event that occurs at a certain time must be "influenced" solely by the outcome of an event that occurs at that time, and that the outcomes of an event that occurs at and at must be conditionally independent of at provided that it occurs at a particular time.


How You Will Benefit


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


Chapter 1: Hidden Markov model


Chapter 2: Markov chain


Chapter 3: Viterbi algorithm


Chapter 4: Expectation-maximization algorithm


Chapter 5: Baum-Welch algorithm


Chapter 6: Metropolis-Hastings algorithm


Chapter 7: Bayesian network


Chapter 8: Gibbs sampling


Chapter 9: Mixture model


Chapter 10: Forward algorithm


(II) Answering the public top questions about hidden markov model.


(III) Real world examples for the usage of hidden markov model 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 hidden markov model.


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The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

作者简介

Fouad Sabry is the former Regional Head of Business Development for Applications at HP in Southern Europe, Middle East, and Africa (SEMEA). Fouad has received his B.Sc. of Computer Systems and Automatic Control in 1996, dual master’s degrees from University of Melbourne (UoM) in Australia, Master of Business Administration (MBA) in 2008, and Master of Management in Information Technology (MMIT) in 2010. 

Fouad has more than 20 years of experience in Information Technology and Telecommunications fields, working in local, regional, and international companies, such as Vodafone and IBM in Middle East and Africa (MEA) region. Fouad joined HP Middle East (ME), based in Dubai, United Arab Emirates (UAE) in 2013 and helped develop the software business in tens of markets across Southern Europe, Middle East, and Africa (SEMEA) regions. Currently, Fouad is an entrepreneur, author, futurist, focused on Emerging Technologies, and Industry Solutions, and founder of One Billion Knowledgeable (1BK) Initiative.

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