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.
Table of Contents (11 chapters)


A lot of work has been done regarding 2D HMMs, but the most recent work and well-received work has been done by Jia Li, Amir Najmi, and Robert Gray in their paper, Image Classification by a Two Dimensional Hidden Markov Model. This section has been written based on their work. We will start by giving the general algorithm they have introduced, and then, in further subsections, we will see how the algorithm works.


The algorithm for image classification is as follows:

  • Training:
    • Divide the training images into non-overlapping blocks with equal size and extract a feature vector for each block
    • Select the number of states for the 2D HMM
    • Estimate the model parameters based on the feature vectors and the training...