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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms.
This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples.
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Seitenzahl: 319
Veröffentlichungsjahr: 2015
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Table of Contents
Mastering Probabilistic Graphical Models Using Python
Mastering Probabilistic Graphical Models Using Python
Copyright © 2015 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 authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be 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.
First published: July 2015
Production reference: 1280715
Published by Packt Publishing Ltd.
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ISBN 978-1-78439-468-4
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Credits
Authors
Ankur Ankan
Abinash Panda
Reviewers
Matthieu Brucher
Dave (Jing) Tian
Xiao Xiao
Commissioning Editor
Kartikey Pandey
Acquisition Editors
Vivek Anantharaman
Sam Wood
Content Development Editor
Gaurav Sharma
Technical Editors
Ankita Thakur
Chinmay S. Puranik
Copy Editors
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Project Coordinator
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Proofreader
Safis Editing
Indexer
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Graphics
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Production Coordinator
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Cover Work
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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.
I would like to thank all the pgmpy contributors who have helped me in bringing it to its current stable state. Also, I would like to thank my parents for their relentless support in my endeavors.
Abinash Panda is an undergraduate from IIT (BHU), Varanasi, and is currently working as a data scientist. He has been a contributor to open source libraries such as the Shogun machine learning toolbox and pgmpy, which he started writing along with four other members. He spends most of his free time on improving pgmpy and helping new contributors.
I would like to thank all the pgmpy contributors. Also, I would like to thank my parents for their support. I am also grateful to all my batchmates of electronics engineering, the class of 2014, for motivating me.
About the Reviewers
Matthieu Brucher holds a master's degree from Ecole Supérieure d'Electricité (information, signals, measures), a master of computer science degree from the University of Paris XI, and a PhD in unsupervised manifold learning from the Université de Strasbourg, France. He is currently an HPC software developer at an oil company and works on next-generation reservoir simulation.
Dave (Jing) Tian is a graduate research fellow and a PhD student in the computer and information science and engineering (CISE) department at the University of Florida. He is a founding member of the Sensei center. His research involves system security, embedded systems security, trusted computing, and compilers. He is interested in Linux kernel hacking, compiler hacking, and machine learning. He also spent a year on AI and machine learning and taught Python and operating systems at the University of Oregon. Before that, he worked as a software developer in the Linux Control Platform (LCP) group at the Alcatel-Lucent (formerly, Lucent Technologies) R&D department for around 4 years. He got his bachelor's and master's degrees from EE in China. He can be reached via his blog at http://davejingtian.org and can be e-mailed at <[email protected]>.
Thanks to the authors of this book for doing a good job. I would also like to thank the editors of this book for making it perfect and giving me the opportunity to review such a nice book.
Xiao Xiao got her master's degree from the University of Oregon in 2014. Her research interest lies in probabilistic graphical models. Her previous project was to use probabilistic graphical models to predict human behavior to help people lose weight. Now, Xiao is working as a full-stack software engineer at Poshmark. She was also the reviewer of Building Probabilistic Graphical Models with Python, Packt Publishing.
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Preface
This book focuses on the theoretical as well as practical uses of probabilistic graphical models, commonly known as PGM. This is a technique in machine learning in which we use the probability distribution over different variables to learn the model. In this book, we have discussed the different types of networks that can be constructed and the various algorithms for doing inference or predictions over these models. We have added examples wherever possible to make the concepts easier to understand. We also have code examples to promote understanding the concepts more effectively and working on real-life problems.
What this book covers
Chapter 1, Bayesian Network Fundamentals, discusses Bayesian networks (a type of graphical model), its representation, and the independence conditions that this type of network implies.
Chapter 2, Markov Network Fundamentals, discusses the other type of graphical model known as Markov network, its representation, and the independence conditions implied by it.
Chapter 3, Inference – Asking Questions to Models, discusses the various exact inference techniques used in graphical models to predict over newer data points.
Chapter 4, Approximate Inference, discusses the various methods for doing approximate inference in graphical models. As doing exact inference in the case of many real-life problems is computationally very expensive, approximate methods give us a faster way to do inference in such problems.
Chapter 5, Model Learning – Parameter Estimation in Bayesian Networks, discusses the various methods to learn a Bayesian network using data points that we have observed. This chapter also discusses the various methods of learning the network structure with observed data.
Chapter 6, Model Learning – Parameter Estimation in Markov Networks, discusses various methods for learning parameters and network structure in the case of Markov networks.
Chapter 7, Specialized Models, discusses some special cases in Bayesian and Markov models that are very widely used in real-life problems, such as Naive Bayes, Hidden Markov models, and others.
What you need for this book
In this book, we have used IPython to run all the code examples. It is not necessary to use IPython but we recommend you to use it. Most of the code examples use pgmpy and sckit-learn. Also, we have used NumPy at places to generate random data.
Who this book is for
This book will be useful for researchers, machine learning enthusiasts, and people who are working in the data science field and have a basic idea of machine learning or graphical models. This book will help readers to understand the details of graphical models and use them in their day-to-day data science problems.
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Installing tools
Let's now see some coding examples using pgmpy, to represent joint distributions and independencies. Here, we will mostly work with IPython and pgmpy (and a few other libraries) for coding examples. So, before moving ahead, let's get a basic introduction to these.
IPython
IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, which offers enhanced introspection, rich media, additional shell syntax, tab completion, and a rich history. IPython provides the following features:
You can install IPython using the following command:
To start the IPython command shell, you can simply type ipython3 in the terminal. For more installation instructions, you can visit http://ipython.org/install.html.
pgmpy
pgmpy is a Python library to work with Probabilistic Graphical models. As it's currently not on PyPi, we will need to build it manually. You can get the source code from the Git repository using the following command:
Now cd into the cloned directory switch branch for version used in this book and build it with the following code:
For more installation instructions, you can visit http://pgmpy.org/install.html.
With both IPython and pgmpy installed, you should now be able to run the examples in the book.
