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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

What listeners say about Markov Models: An Introduction to Markov Models

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Fantastic!

Markov chains are an important topic in statistics, with numerous applications in computing and engineering. If you have never dealt with these, and desire an advanced text, then Steven Taylor's book can be of interest.

It explains key ideas, like a transition matrix. Which is usually defined for a discrete-time Markov process, but can in fact be generalized to infinite-dimensional state space. From the matrix approach, we get a transition graph and cycles. Then, ergodic behavior is studied, with invariant measures being found to characterize a given chain.

Taylor also covers applications of Markov chains. He treats phase transitions and spontaneous magnetization. Then there are queuing problems and Monte Carlo simulations. The latter can be used in simulated annealing; which in turn can be put to a wide range of problems.

4 people found this helpful

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    5 out of 5 stars
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    5 out of 5 stars

Lots of things learned

Through this book, I've learned that Markov models are valuable once a decision problem entails the risk that is constant over time when the scheduling of events is imperative. In addition, Markov models are also very valuable when vital events may take place more than once. Coming up with proper representations of such sensitive settings with traditional decision trees is complex and may call for impracticable simplifying assumptions.

The author will show you how to be a data master. You’ll discover how to solve almost-unsolvable machine learning problems in no time. I’m going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across.

It’s never been easier to make predictions and smart analysis with the use of Markov Models. You don’t need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills, and a decent knowledge of Python programming in order to solve the most perplexing problems.

If you want to know more about Markov Models, then I recommend it to you.

4 people found this helpful

  • Overall
    5 out of 5 stars
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    5 out of 5 stars
  • Story
    5 out of 5 stars

Excellent book

A comprehensive book about Markov models. You need to be mathematically very strong to get a grasp of the material and you might need help to make practical implementable models.

I am a Ph.D. student in Statistics and recently bought this book for background reading related to my research in credit risk modeling with latent variables. The authors were able to produce a very readable treatment of Markov Chains, Monte Carlo methods, and the EM algorithm. The book contains plenty of examples of finance and communications theory.

Overall, I am very pleased with my decision to buy this book. If you are looking for a guide to help you learn Markov Models, don't miss this book.

3 people found this helpful

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Found it useful

I was happy to find this book for a good price. Now I have what I need to do my research. For anyone looking for an introduction to classic discrete state, discrete action Markov decision processes this is the last in a long line of books on this theory, and the only book you will need.

1 person found this helpful

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Practical

I have found this book to be one of the best practical books for Bayesian analysis. If every statistics textbook were more like Steven's work, I would have become a statistician. It's made me rethink how textbooks ought to be written, and it's an antidote to those pedagogical steakburgers that many of us had to fight our way through during grad school.

1 person found this helpful

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    5 out of 5 stars

Great content

This was a solid quick start to understanding the fundamentals of Markov chains. Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of memorylessness, stating that the outcome of a problem depends only on the current state of the system - historical data must be ignored.

1 person found this helpful

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    5 out of 5 stars
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    5 out of 5 stars

Contains useful and interesting information

Markov Models has become a pillar of information technology. Here you will find a lot of information that is given for a person to find resources for self-study. This is a good book for all beginners. Easy to listen to and understand for everybody, contains useful and interesting information.

1 person found this helpful

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    5 out of 5 stars

Informative book.

A very informative book. Good advice and tips that work and a great beginner's guide for me. I have learned so much from it. I recommend and thank you to the author

1 person found this helpful

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    5 out of 5 stars

Detailed explanation

Anyone working with Markov Decision Processes should have this book. It has detailed explanations of the hidden Markov models, dynamic bayesian networks, stepwise mutation using the wright fisher model, using normalized algorithms to update formulas, and more. However, it does not cover some new ideas like partitioning and some faster-approximated algorithms. But still, it is a great book!

1 person found this helpful

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    5 out of 5 stars

Informative

The presentation covers this elegant theory very thoroughly, including all the major problem classes (finite and infinite horizon, discounted reward, average reward). The presentation is rigorous, and while it will be best appreciated by doctoral students and the research community, most of the presentation can be easily understood by a master's audience with a strong background in probability.

1 person found this helpful

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  • Jennifer Barnes
  • 22-01-21

Recommended

If you want to know more about Markov Models, then I recommend it to you. Through this book, I've learned that Markov models are valuable once a decision problem entails the risk that is constant over time when the scheduling of events is imperative. In addition, Markov models are also very valuable when vital events may take place more than once. Coming up with proper representations of such sensitive settings with traditional decision trees is complex and may call for impracticable simplifying assumptions.

The author will show you how to be a data master. You’ll discover how to solve almost-unsolvable machine learning problems in no time. I’m going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across.

It’s never been easier to make predictions and smart analysis with the use of Markov Models. You don’t need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills, and a decent knowledge of Python programming in order to solve the most perplexing problems.

4 people found this helpful

  • Overall
    5 out of 5 stars
  • Performance
    5 out of 5 stars
  • Story
    5 out of 5 stars
Profile Image for Elizabeth Wood
  • Elizabeth Wood
  • 19-01-21

Lots of things learned

Markov chains are an important topic in statistics, with numerous applications in computing and engineering. If you have never dealt with these, and desire an advanced text, then Steven Taylor's book can be of interest.

It explains key ideas, like a transition matrix. Which is usually defined for a discrete-time Markov process, but can in fact be generalized to infinite-dimensional state space. From the matrix approach, we get a transition graph and cycles. Then, ergodic behavior is studied, with invariant measures being found to characterize a given chain.

Taylor also covers applications of Markov chains. He treats phase transitions and spontaneous magnetization. Then there are queuing problems and Monte Carlo simulations. The latter can be used in simulated annealing; which in turn can be put to a wide range of problems.

3 people found this helpful

  • Overall
    5 out of 5 stars
  • Performance
    5 out of 5 stars
  • Story
    5 out of 5 stars
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  • Susan Henderson
  • 29-12-20

A comprehensive book about Markov models

A comprehensive book about Markov models. You need to be mathematically very strong to get a grasp of the material and you might need help to make practical implementable models.

I am a Ph.D. student in Statistics and recently bought this book for background reading related to my research in credit risk modeling with latent variables. The authors were able to produce a very readable treatment of Markov Chains, Monte Carlo methods, and the EM algorithm. The book contains plenty of examples of finance and communications theory.

Overall, I am very pleased with my decision to buy this book. If you are looking for a guide to help you learn Markov Models, don't miss this book.

3 people found this helpful

  • Overall
    5 out of 5 stars
  • Performance
    5 out of 5 stars
  • Story
    5 out of 5 stars
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  • Larry Peterson
  • 24-01-21

Thumbs up!

This is a must-have book for everybody interested in learning Bayesian statistics. The book is incredibly well written from start to end, the online lectures are also a good complement. I doubt you would want to go back using classical statistical methods after reading this book. Highly recommended. If Steven were to publish a second book on this topic tomorrow, I'd buy it sight unseen, at full price. There's no better recommendation for a textbook than that.

2 people found this helpful

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  • Kenneth Dee
  • 20-01-21

Extremely comprehensive

If you don't like mathematical equations, derivations, and proofs using algebraic manipulation, you will hate this book. The book is written more like a manual of methods and their justifications; do not expect the author to hold your hand throughout every step in every derivation. Extremely comprehensive and very useful for anyone serious about studying probability.

2 people found this helpful

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    5 out of 5 stars
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    5 out of 5 stars
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    5 out of 5 stars
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  • Linda Price
  • 25-01-21

In detail

Anyone working with Markov Decision Processes should have this book. It has detailed explanations of the hidden Markov models, dynamic bayesian networks, stepwise mutation using the wright fisher model, using normalized algorithms to update formulas, and more. However, it does not cover some new ideas like partitioning and some faster-approximated algorithms. But still, it is a great book!

1 person found this helpful

  • Overall
    5 out of 5 stars
  • Performance
    5 out of 5 stars
  • Story
    5 out of 5 stars
Profile Image for Ruthille Chua
  • Ruthille Chua
  • 24-01-21

Very thorough

This was a solid quick start to understanding the fundamentals of Markov chains. Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of memorylessness, stating that the outcome of a problem depends only on the current state of the system - historical data must be ignored.

1 person found this helpful

  • Overall
    5 out of 5 stars
  • Performance
    5 out of 5 stars
  • Story
    5 out of 5 stars
Profile Image for Fajardo R.
  • Fajardo R.
  • 24-01-21

Good book for the beginners

Markov Models has become a pillar of information technology. Here you will find a lot of information that is given for a person to find resources for self-study. This is a good book for all beginners. Easy to listen and understand for everybody, contains useful and interesting information.

1 person found this helpful

  • Overall
    5 out of 5 stars
  • Performance
    5 out of 5 stars
  • Story
    5 out of 5 stars
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  • C. Walker
  • 07-01-21

Concise and clear overview

A very brief overview of the subject matter. Good for someone only starting to gather knowledge about Markov models. It looks like Artificial Intellect is the next step in human technological development. For me, unsupervised learning of computers is pretty similar to human brain functioning. Analyzing each formula will make it easier for you to understand the problem.

1 person found this helpful

  • Overall
    5 out of 5 stars
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    5 out of 5 stars
  • Story
    5 out of 5 stars
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  • Thea G.
  • 07-01-21

Provides broad understanding of the models

This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.

1 person found this helpful