Search query: probability graphical models

Product: Graphical Models, Exponential Families, and Variational Inference (Foundations and Trends(r) in Machine Learning)
Brand: Now Publishers Inc
Used Book in Good Condition
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Product: Handbook of Graphical Models (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
Description: A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference.

While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art.

Key features:

* Contributions by leading researchers from a range of disciplines

* Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications

* Balanced coverage of concepts, theory, methods, examples, and applications

* Chapters can be read mostly independently, while cross-references highlight connections

The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.
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Product: Learning Probabilistic Graphical Models in R
Description: Key Features

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

Book Description

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.

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

About the Author

David Bellot is a PhD graduate in computer science from INRIA, France, with a focus on Bayesian machine learning. He was a postdoctoral fellow at the University of California, Berkeley, and worked for companies such as Intel, Orange, and Barclays Bank. He currently works in the financial industry, where he develops financial market prediction algorithms using machine learning. He is also a contributor to open source projects such as the Boost C++ library.

Table of Contents

Probabilistic Reasoning
Exact Inference
Learning Parameters
Bayesian Modeling – Basic Models
Approximate Inference
Bayesian Modeling – Linear Models
Probabilistic Mixture Models
Appendix
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Product: Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling
Description: Probabilistic Graphical Models gives an overview of PGMs (a framework encompassing techniques like bayesian networks, markov random fields and chain graphs), which incorporate forward-looking information for making financial decisions, and applies them to stress testing, asset allocation, hedging, and credit risk.
This approach describes a new way to contend with stress testing (a big component of regulations like CCAR, the AIFMD, and Solvency II), teaches the reader how to strengthen their portfolios, presents a forward-looking way of conducting tail hedging, and gives a clear picture of the credit risk of the institution in question (such as a bank or a hedge fund).
Probabilistic Graphical Models teaches this relatively new technique to the reader, explaining how it can be applied to a variety of everyday challenges. Previous to their use in finance, PGMs have been used in disciplines such as computer science, engineering and medicine. Author Alexander Denev expands on this pre-existing material to examine other types of PGMs, demonstrating a novel range of applications
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Product: Mastering Probabilistic Graphical Models Using Python
Description: Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

About This Book

Gain in-depth knowledge of Probabilistic Graphical Models
Model time-series problems using Dynamic Bayesian Networks
A practical guide to help you apply PGMs to real-world problems

Who This Book Is For

If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem.

What You Will Learn

Get to know the basics of Probability theory and Graph Theory
Work with Markov Networks
Implement Bayesian Networks
Exact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm
Understand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms
Sample algorithms in Graphical Models
Grasp details of Naive Bayes with real-world examples
Deploy PGMs using various libraries in Python
Gain working details of Hidden Markov Models with real-world examples

In Detail

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.

Style and approach

An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.
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Product: Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Product: Graphical Models with R (Use R!)
Brand: Springer
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Product: Introduction to High-Dimensional Statistics (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Brand: Chapman and Hall/CRC
CRC Press
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Product: Graphical Models in Applied Multivariate Statistics (Wiley Series in Probability and Statistics)
Brand: Wiley
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Product: Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
Brand: Springer
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Product: Introduction to Imprecise Probabilities (Wiley Series in Probability and Statistics)
Brand: Wiley
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Product: Graphical Models: Methods for Data Analysis and Mining
Brand: Wiley
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Product: Graphical Models: Foundations of Neural Computation (Computational Neuroscience)
Brand: A Bradford Book
Color: Green
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Product: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Brand: The MIT Press
Color: Navy
MIT Press MA
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Product: Probabilistic Graphical Models for Genetics, Genomics and Postgenomics
Brand: Oxford University Press, USA
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Product: Graphical Models (Oxford Statistical Science Series, 17)
Brand: OUP Oxford
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