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Probabilistic Graphical Models


Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009



Download Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.


A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

Probabilistic Graphical Models


Author : Luis Enrique Sucar
language : en
Publisher: Springer
Release Date : 2015-06-19



Download Probabilistic Graphical Models written by Luis Enrique Sucar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Computers categories.


This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.

Advances In Probabilistic Graphical Models


Author : Peter Lucas
language : en
Publisher: Springer
Release Date : 2009-09-02



Download Advances In Probabilistic Graphical Models written by Peter Lucas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-02 with Mathematics categories.


This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

Probabilistic Graphical Models For Genetics Genomics And Postgenomics


Author : Christine Sinoquet
language : en
Publisher: Oxford University Press, USA
Release Date : 2014



Download Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Christine Sinoquet and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Mathematics categories.


At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. For instance, Bayesian networks and Markov random fields are two of the most popular probabilistic graphical models. With the rapid advance of high-throughput technologies and their ever decreasing costs, a fast-growing volume of biological data of various types - the so-called ''omics'' - is in need of accurate andefficient methods for modeling, prior to further downstream analysis. As probabilistic graphical models are able to deal with high-dimensional data, it is foreseeable that such models will have aprominent role to play in advances in genome-wide data analyses. Currently, few people are specialists in the design of cutting-edge methods using probabilistic graphical models for genetics, genomics and postgenomics. This seriously hinders the diffusion of such methods. The prime aim of the book is therefore to bring the concepts underlying these advanced models within reach of scientists who are not specialists of these models, but with no concession on theinformativeness of the book. The target readers include researchers and engineers who have to design novel methods for postgenomics data analysis, as well as graduate students starting a Masters or a PhD. Inaddition to an introductory chapter on probabilistic graphical models, a thorough review chapter focusing on selected domains in genetics and fourteen chapters illustrate the design of such advanced approaches in various domains: gene network inference, inference of causal phenotype networks, association genetics, epigenetics, detection of copy number variations, and prediction of outcomes from high-dimensional genomic data. Notably, most examples also illustrate that probabilistic graphicalmodels are well suited for integrative biology and systems biology, hot topics guaranteed to be of lasting interest.

Probabilistic Graphical Models


Author : Linda C. van der Gaag
language : en
Publisher: Springer
Release Date : 2014-09-11



Download Probabilistic Graphical Models written by Linda C. van der Gaag and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-11 with Computers categories.


This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.

Building Probabilistic Graphical Models With Python


Author : Kiran R Karkera
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-06-25



Download Building Probabilistic Graphical Models With Python written by Kiran R Karkera 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 2014-06-25 with Computers categories.


This is a short, practical guide that allows data scientists to understand the concepts of Graphical models and enables them to try them out using small Python code snippets, without being too mathematically complicated. If you are a data scientist who knows about machine learning and want to enhance your knowledge of graphical models, such as Bayes network, in order to use them to solve real-world problems using Python libraries, this book is for you.This book is intended for those who have some Python and machine learning experience, or are exploring the machine learning field.

Learning Probabilistic Graphical Models In R


Author : David Bellot
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-04-29



Download Learning Probabilistic Graphical Models In R written by David Bellot 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 2016-04-29 with Computers categories.


Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book Predict and use a probabilistic graphical models (PGM) as an expert system Comprehend how your computer can learn Bayesian modeling to solve real-world problems Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package Who This Book Is For This book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting. What You Will Learn Understand the concepts of PGM and which type of PGM to use for which problem Tune the model's parameters and explore new models automatically Understand the basic principles of Bayesian models, from simple to advanced Transform the old linear regression model into a powerful probabilistic model Use standard industry models but with the power of PGM Understand the advanced models used throughout today's industry See how to compute posterior distribution with exact and approximate inference algorithms In Detail Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models. We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction. Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems. Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.