Python script and Documents. Contribute to huwenyuan/Python-1 development by creating an account on GitHub. Building Probabilistic Graphical Models with Python. Copyright . Did you know that Packt offers eBook versions of every book published, with PDF and ePub. Building Probabilistic Graphical Models with Python The_Tiger__ A_True_Story_of_Vengeance_and_S_-_Vaillant,gepbansbassmenve.ga The Tiger: A True Story of.
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Solve machine learning problems using probabilistic graphical models implemented in Python, with real-world applications. Building Probabilistic Graphical. Models with Python. Solve machine learning problems using probabilistic graphical models implemented in Python with. Building Probabilistic Graphical Models with Python b Solve machine learning problems using probabilistic graphical models implemented in Python with real.
We will then try to understand the types of questions that probability can help us answer and the multiple interpretations of probability. Finally, we will take a quick look at the Bayes rule, which helps us understand the relationships between probabilities, and also look at the accompanying concepts of conditional probabilities and the chain rule.
We often encounter situations where we have to exercise our subjective belief about an event's occurrence; for example, events such as weather or traffic that are inherently stochastic. Probability can also be understood as the degree of subjective belief. When we talk about the weather for example, this evening , it is understood that the weather can have multiple outcomes such as rainy, sunny, or cloudy. The space of all the possible outcomes is said to be an event also called the sample space.
For example, the outcomes of a throw of a dice would be a set of numbers from 1 to 6. While dealing with measurable outcomes such as the throw of a dice or today's weather which can be rainy, sunny, or cloudy , we can assign a probability value to each outcome to encapsulate our degree of belief in those outcomes. The idea of a fair coin translates to the fact that the controlling parameter has a value of 0.
Later in the book, we shall examine how many parameters are required to completely specify a probability distribution.
Thanks to the author of this book who has done a good job for both Python and PGM; thanks to the editors of this book, who have made this book perfect and given me the opportunity to review such a nice book.
Her research interests lie in machine learning, especially probabilistic graphical models. Her previous project was to compare two inference algorithms' performance on a graphical model relational dependency network. PacktP Support files, eBooks, discount offers and more You might want to visit www.
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PacktLib is Packt's online digital book library. Here, you can access, read and search across Packt's entire library of books. Why Subscribe? PacktP, you can use this to access PacktLib today and view nine entirely free books. Simply use your login credentials for immediate access. We then explore subproblems in the context of graphical models, such as their representation, building them, learning their structure and parameters, and using them to answer our inference queries.
This book attempts to give just enough information on the theory, and then use code samples to peep under the hood to understand how some of the algorithms are implemented. The code sample also provides a handy template to build graphical models and answer our probability queries.
Of the many kinds of graphical models described in the literature, this book primarily focuses on discrete Bayesian networks, with occasional examples from Markov networks. What this book covers Chapter 1, Probability, covers the concepts of probability required to understand the graphical models.
Chapter 2, Directed Graphical Models, provides information about Bayesian networks, their properties related to independence, conditional independence, and D-separation. This chapter uses code snippets to load a Bayes network and understand its independence properties. Chapter 3, Undirected Graphical Models, covers the properties of Markov networks, how they are different from Bayesian networks, and their independence properties. Chapter 4, Structure Learning, covers multiple approaches to infer the structure of the Bayesian network using a dataset.
We also learn the computational complexity of structure learning and use code snippets in this chapter to learn the structures given in the sampled datasets. Preface Chapter 5, Parameter Learning, covers the maximum likelihood and Bayesian approaches to parameter learning with code samples from PyMC.
Chapter 6, Exact Inference Using Graphical Models, explains the Variable Elimination algorithm for accurate inference and explores code snippets that answer our inference queries using the same algorithm. Chapter 7, Approximate Inference Methods, explores the approximate inference for networks that are too large to run exact inferences on. We will also go through the code samples that run approximate inferences using loopy belief propagation on Markov networks.
Appendix, References, includes all the links and URLs that will help to easily understand the chapters in the book. What you need for this book To run the code samples in the book, you'll need a laptop or desktop with IPython installed.
Who this book is for This book is aimed at developers conversant with Python and who wish to explore the nuances of graphical models using code samples. This book is also ideal for students who have been theoretically introduced to graphical models and wish to realize the implementations of graphical models and get a feel for the capabilities of different graphical model libraries to deal with real- world models.
Machine-learning practitioners familiar with classification and regression models and who wish to explore and experiment with the types of problems graphical models can solve will also find this book an invaluable resource.
This book looks at graphical models as a tool that can be used to solve problems in the machine-learning domain.
Moreover, it does not attempt to explain the mathematical underpinnings of graphical models or go into details of the steps for each algorithm used. Here are some examples of these styles, and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can do the same by creating a TfidfVectorizer object.
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