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Hands-On Markov Models with Python

You're reading from Hands-On Markov Models with Python

Product type Book

Published in Sep 2018

Publisher Packt

ISBN-13 9781788625449

Pages 178 pages

Edition 1st Edition

Languages

Concepts

Authors (2):

Ankur Ankan

Abinash Panda

View More author details

Table of Contents (11) Chapters

Preface

1. Introduction to the Markov Process

2. Hidden Markov Models

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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Introduction to the Markov Process

In this chapter, we will develop the basic concepts that we need to understand Hidden Markov Models (HMM). We will cover the following topics:

Random processes
Markov processes
Markov chains or discrete-time Markov processes
Continuous-time Markov chains

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Hands-On Markov Models with Python

Published in: Sep 2018 Publisher: Packt ISBN-13: 9781788625449

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Authors (2)

Ankur 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.

See other products by Ankur Ankan

Abinash 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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