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Hands-On Markov Models with Python

You're reading from Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem

Product type Paperback

Published in Sep 2018

Publisher Packt

ISBN-13 9781788625449

Length 178 pages

Edition 1st Edition

Languages

Python

Concepts

Statistics

Authors (2):

Ankur Ankan

Abinash Panda

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Table of Contents (11) Chapters

Preface

1. Introduction to the Markov Process

2. Hidden Markov Models FREE CHAPTER

3. State Inference - Predicting the States

4. Parameter Learning Using Maximum Likelihood

5. Parameter Inference Using the Bayesian Approach

6. Time Series Predicting

7. Natural Language Processing

8. 2D HMM for Image Processing

9. Markov Decision Process

10. Other Books You May Enjoy

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Summary

In this chapter, we gave a detailed introduction to Markov chains. We talked about different types of Markov chains, mainly chains with a discrete state space, with either discrete time or continuous time. We also introduced the concepts of time-homogeneous and non-time-homogeneous Markov chains. We discussed the different properties of Markov chains in detail, and provided relevant examples and code.

Markov chains and their properties are the basic concepts on which HMMs are built. In the next chapter, we will discuss HMMs in much more detail.

The rest of the chapter is locked

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Authors (2)

Ankan

Ankan

Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions.

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Panda

Panda

Abinash Panda has been a data scientist for more than 4 years. He has worked at multiple early-stage start-ups and helped them build their data analytics pipelines. He loves to munge, plot, and analyze data. He has been a speaker at Python conferences. These days, he is busy co-founding a start-up. He has contributed to books on probabilistic graphical models by Packt Publishing.

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