Book Image

Hands-On Markov Models with Python

By : Ankur Ankan, Abinash Panda
Book Image

Hands-On Markov Models with Python

By: Ankur Ankan, Abinash Panda

Overview of this book

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.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to the Markov Process
Introduction to the Markov Process
Random variables
Random processes
Markov processes
Installing Python and packages
Markov chains or discrete-time Markov processes
Continuous-time Markov chains
Summary
2
Hidden Markov Models
Hidden Markov Models
Markov models
The HMM
Evaluation of an HMM
Extensions of HMM
Summary
3
State Inference - Predicting the States
State Inference - Predicting the States
State inference in HMM
Dynamic programming
Forward algorithm
Backward algorithm
Forward-backward algorithm (smoothing)
The Viterbi algorithm
Summary
4
Parameter Learning Using Maximum Likelihood
Parameter Learning Using Maximum Likelihood
Maximum likelihood learning
MLE for HMMs
Summary
5
Parameter Inference Using the Bayesian Approach
Parameter Inference Using the Bayesian Approach
Bayesian learning
Bayesian learning in HMM
Code
Summary
6
Time Series Predicting
Time Series Predicting
Stock price prediction using HMM
Summary
7
Natural Language Processing
Natural Language Processing
Part-of-speech tagging
Speech recognition
Summary
8
2D HMM for Image Processing
2D HMM for Image Processing
Recap of 1D HMM
2D HMMs
Summary
9
Markov Decision Process
Markov Decision Process
Reinforcement learning
The Markov reward process
Code example
Summary
10
Other Books You May Enjoy
Other Books You May Enjoy
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To get the most out of this book

You need to have Python 3.4 installed on your machine in order to work through the chapters smoothly.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Markov-Models-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Now, let's define the MFCTagger class."

A block of code is set as follows:

from hmmlearn.hmm import GaussianHMM
import numpy as np
import matplotlib.pyplot as plt

Any command-line input or output is written as follows:

pip install matplotlib datetime

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "The possible states of the outcomes are also known as the domain of the random variable."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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