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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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