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    Title Page
    Copyright and Credits
    Hands-On Markov Models with Python
    Packt Upsell
    Why subscribe?
    packt.com
    Contributors
    About the authors
    About the reviewer
    Packt is searching for authors like you
    Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Reviews
    Introduction to the Markov Process
    Random variables
    Random processes
    Markov processes
    Installing Python and packages
    Installation on Windows
    Installation on Linux
    Markov chains or discrete-time Markov processes
    Parameterization of Markov chains
    Properties of Markov chains
    Reducibility
    Periodicity
    Transience and recurrence
    Mean recurrence time
    Expected number of visits
    Absorbing states
    Ergodicity
    Steady-state analysis and limiting distributions
    Continuous-time Markov chains
    Exponential distributions
    Poisson process
    Continuous-time Markov chain example
    Continuous-time Markov chain
    Summary
    Hidden Markov Models
    Markov models
    State space models
    The HMM
    Parameterization of HMM
    Generating an observation sequence
    Installing Python packages
    Evaluation of an HMM
    Extensions of HMM
    Factorial HMMs
    Tree-structured HMM
    Summary
    State Inference - Predicting the States
    State inference in HMM
    Dynamic programming
    Forward algorithm
    Computing the conditional distribution of the hidden state given the observations
    Backward algorithm
    Forward-backward algorithm (smoothing)
    The Viterbi algorithm
    Summary
    Parameter Learning Using Maximum Likelihood
    Maximum likelihood learning
    MLE in a coin toss
    MLE for normal distributions
    MLE for HMMs
    Supervised learning
    Code
    Unsupervised learning
    Viterbi learning algorithm
    The Baum-Welch algorithm (expectation maximization)
    Code
    Summary
    Parameter Inference Using the Bayesian Approach
    Bayesian learning
    Selecting the priors
    Intractability
    Bayesian learning in HMM
    Approximating required integrals
    Sampling methods
    Laplace approximations
    Stolke and Omohundro's method
    Variational methods
    Code
    Summary
    Time Series Predicting
    Stock price prediction using HMM
    Collecting stock price data
    Features for stock price prediction
    Predicting price using HMM
    Summary
    Natural Language Processing
    Part-of-speech tagging
    Code
    Getting data
    Exploring the data
    Finding the most frequent tag
    Evaluating model accuracy
    An HMM-based tagger
    Speech recognition
    Python packages for speech recognition
    Basics of SpeechRecognition
    Speech recognition from audio files
    Speech recognition using the microphone
    Summary
    2D HMM for Image Processing
    Recap of 1D HMM
    2D HMMs
    Algorithm
    Assumptions for the 2D HMM model
    Parameter estimation using EM
    Summary
    Markov Decision Process
    Reinforcement learning
    Reward hypothesis
    State of the environment and the agent
    Components of an agent
    The Markov reward process
    Bellman equation
    MDP
    Code example
    Summary
    Other Books You May Enjoy
    Leave a review - let other readers know what you think
    Hands-On Markov Models with Python Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankur Ankan Abinash Panda BIRMINGHAM - MUMBAI
    Hands-On Markov Models with Python Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Commissioning Editor: Sunith Shetty Acquisition Editor: Varsha Shetty Content Development Editor: Karan Thakkar Technical Editor: Sagar Sawant Copy Editor: Safis Editing Project Coordinator: Nidhi Joshi Proofreader: Safis Editing Indexer: Aishwarya Gangawane Graphics: Jisha Chirayil Production Coordinator: Shraddha Falebhai First published: September 2018 Production reference: 1250918 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78862-544-9 www.packtpub.com
    mapt.io Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.
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    Contributors
    About the authors 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. 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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