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Publisher's Summary

Markov Models
This book will offer you an insight into the hidden Markov Models as well as the Bayesian networks. Additionally, by listening to this book, you will also learn algorithms such as Markov chain sampling.
Furthermore, this book will also teach you how Markov models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrences is vital, as well as when events occur more than once. This book highlights several applications of Markov models.
Lastly, after purchasing this book, you will need to put in a lot of effort and time for you to reap the maximum benefits.
By listening to this book now, you will discover:


Hidden Markov models
Dynamic Bayesian networks
Stepwise mutations using the Wright Fisher model
Using normalized algorithms to update the formulas
Types of Markov processes
Important tools used with HMM
Machine learning
And much, much more!

Download this book now, and learn more about Markov models!
©2017 Steven Taylor (P)2017 Steven Taylor
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Customer Reviews

Most Helpful
5 out of 5 stars
By dylan on 10-10-17

This book provides general information and how to

This book provides general information and how to use it in real life. This is an informative guide for the reader indeed. The Markov models give amazing flexibility in arranging of events.This is quite interesting. I learned Markov Models from this book.

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5 out of 5 stars
By Olivia on 10-10-17

Markov models

This is a very good book about what Markov models is and how it operates. It is a quick read and to the point. Get some background insight on what machines can learn on their own and how it is applied in practical life.

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Customer Reviews

Most Helpful
5 out of 5 stars
By lAYANA on 03-26-18

NICE BOOK TO HAVE

Very nice book with lots of useful information and enough case studies and learning materials. Best book.

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4 out of 5 stars
By sana on 03-26-18

Good

The book is very well categorized into several sections and each section contains chapters related to various aspects of Markov Models.

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