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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

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Table of Contents (11) Chapters

Preface

Introduction to the Markov Process

Hidden Markov Models

State Inference - Predicting the States

Parameter Learning Using Maximum Likelihood

Parameter Inference Using the Bayesian Approach

Time Series Predicting

Natural Language Processing

2D HMM for Image Processing

Markov Decision Process

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Code

Currently, there are no packages in Python that support learning using Bayesian learning and it would be really difficult to write the complete code to fit in this book. And even though there are a lot of advantages to using Bayesian learning, it is usually computationally infeasible in a lot of cases. For these reasons, we are skipping the code for Bayesian learning in HMMs.

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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.

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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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