Learning Bayesian Networks
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Learning Bayesian Networks
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Author : Richard E. Neapolitan
language : en
Publisher: Prentice Hall
Release Date : 2004
Learning Bayesian Networks written by Richard E. Neapolitan and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Bayesian Networks
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Author : Marco Scutari
language : en
Publisher: CRC Press
Release Date : 2014-06-20
Bayesian Networks written by Marco Scutari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-20 with Computers categories.
Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.
Approximation Methods For Efficient Learning Of Bayesian Networks
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Author : C. Riggelsen
language : en
Publisher: IOS Press
Release Date : 2008-01-15
Approximation Methods For Efficient Learning Of Bayesian Networks written by C. Riggelsen and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-15 with Computers categories.
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. Topics discussed are; basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way material previously published by the author, with unpublished work.
Learning Bayesian Models With R
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Author : Dr. Hari M. Koduvely
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-10-28
Learning Bayesian Models With R written by Dr. Hari M. Koduvely and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-28 with Computers categories.
Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book Understand the principles of Bayesian Inference with less mathematical equations Learn state-of-the art Machine Learning methods Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide Who This Book Is For This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R. What You Will Learn Set up the R environment Create a classification model to predict and explore discrete variables Get acquainted with Probability Theory to analyze random events Build Linear Regression models Use Bayesian networks to infer the probability distribution of decision variables in a problem Model a problem using Bayesian Linear Regression approach with the R package BLR Use Bayesian Logistic Regression model to classify numerical data Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing In Detail Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results. Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R. Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter. The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks. Style and approach The book first gives you a theoretical description of the Bayesian models in simple language, followed by details of its implementation in the R package. Each chapter has illustrations for the use of Bayesian model and the corresponding R package, using data sets from the UCI Machine Learning repository. Each chapter also contains sufficient exercises for you to get more hands-on practice.
How To Fine Tune Bayesian Networks For Classification
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Author : Ionut B. Brandusoiu
language : en
Publisher: GAER Publishing House
Release Date : 2020-08-19
How To Fine Tune Bayesian Networks For Classification written by Ionut B. Brandusoiu and has been published by GAER Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-19 with Computers categories.
This book covers in the first part the theoretical aspects of Bayesian networks and their functionality, and then based on the discussed concepts it explains how to find-tune a Bayesian network to yield highly accurate prediction results which are adaptable to any classification tasks. The introductory part is extremely beneficial to someone new to learning Bayesian networks, while the more advanced notions are useful for everyone who wants to understand the mathematics behind Bayesian networks and how to find-tune them in order to generate the best predictive performance of a certain classification model.
Advanced Methodologies For Bayesian Networks
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Author : Joe Suzuki
language : en
Publisher: Springer
Release Date : 2016-01-07
Advanced Methodologies For Bayesian Networks written by Joe Suzuki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-07 with Computers categories.
This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015. The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.
Learning From Data
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Author : Doug Fisher
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-05-02
Learning From Data written by Doug Fisher and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-05-02 with Computers categories.
This volume contains a revised collection of papers originally presented at the Fifth International Workshop on Artificial Intelligence and Statistics in 1995. The topics represented in this volume are diverse, and include natural language application causality and graphical models, classification, learning, knowledge discovery, and exploratory data analysis. The chapters illustrate the rich possibilities for interdisciplinary study at the interface of artificial intelligence and statistics. The chapters vary in the background that they assume, but moderate familiarity with techniques of artificial intelligence and statistics is desirable in most cases.
Advances In Bayesian Networks
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Author : José A. Gámez
language : en
Publisher: Springer
Release Date : 2013-06-29
Advances In Bayesian Networks written by José A. Gámez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
Modeling And Reasoning With Bayesian Networks
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Author : Adnan Darwiche
language : en
Publisher: Cambridge University Press
Release Date : 2009-04-06
Modeling And Reasoning With Bayesian Networks written by Adnan Darwiche and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-06 with Computers categories.
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
Bayesian Networks In R
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Author : Radhakrishnan Nagarajan
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-07-08
Bayesian Networks In R written by Radhakrishnan Nagarajan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-08 with Computers categories.
Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.