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Hidden Markov Models

In the previous chapter, we discussed Markov chains, which are helpful in modelling a sequence of observations across time. In this chapter, we are going to study the Hidden Markov Model (HMM), which is also used to model sequential data but is much more flexible than Markov chains.

In this chapter, we will cover the following topics:

Markov models
The HMM
Evaluation of an HMM
Extensions of HMM

Key benefits

Build a variety of Hidden Markov Models (HMM)

Create and apply models to any sequence of data to analyze, predict, and extract valuable insights

Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation

Description

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.

Who is this book for?

Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book

What you will learn

Explore a balance of both theoretical and practical aspects of HMM

Implement HMMs using different datasets in Python using different packages

Understand multiple inference algorithms and how to select the right algorithm to resolve your problems

Develop a Bayesian approach to inference in HMMs

Implement HMMs in finance, natural language processing (NLP), and image processing

Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Victoria Sherratt Jul 10, 2021

It's ok, not great. It's printed by Amazon and some of the graphics are a bit low quality. What annoyed me most about this book is the chapter I was most interested in, the authors didn't bother with the code - "it would be too long for this book" - but in later chapters presented pages of back to back code. The book is not long at all so why skirt over it?

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Damian Jan Cordon Jun 26, 2019

Las fórmulas y gráficos de este libro son diminutas en el Kindle y no es posible aumentar su tamaño, lo que hace imposible seguir correctamente los razonamientos que aplica ya que no es posible acceder a la justificacion matemática de lo que explica.

RFEMYGDIO Jan 21, 2019

Excelente

DWH Dec 02, 2018

Although the algorithms in this book are generally correct it is riddled with crippling errors. There are undefined variables and out of range errors in almost every example. These are still present in the code that you download directly from the publisher. Buyer beware, you'll spend more time troubleshooting than learning.

Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

What do you get with Print?

Hands-On Markov Models with Python

Hidden Markov Models

Markov models

The HMM

Evaluation of an HMM

Extensions of HMM

Factorial HMMs

Summary

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Description

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What you will learn

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Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

People who bought this also bought

About the authors

FAQs