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)

State inference in HMM

Let's start with a simple example to show what kind of interesting questions we can ask our HMM models. We are taking an example of robot localization. There are a lot of variations of this example, but we are assuming that a robot is moving in a 2D grid, as shown in Figure 3.1. The robot also has four sensors on it. Each of these sensors detects whether there's a wall right next to the robot in the sensor's direction.

We would like to model the movement of the robot in the following grid along with the observations from our sensors:

Figure 3.1: The probability distribution of the position of the robot over time

In Figure 3.1, we see how the observations at different time instances change the probability of the location of the robot in the grid. Initially, we start with a uniform probability over all the positions in the grid. Now, at time...