Probabilistic Graphical Models – Principles and Techniques - Image 1

£74.48

MIT Press Probabilistic Graphical Models – Principles and Techniques

Name: Probabilistic Graphical Models - Principles and Techniques
Brand: MIT Press
Price: 74.48 GBP
Availability: InStock

Navy

Higher Education

It has never been this cheap. We have no record of a lower price.

£74 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 45 days • 45 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 45 days • 3 price levels

Days at Price

Current Price

Price Analysis

Most common price: £74 (30 days, 66.7%)

Price range: £74 - £84

Price levels: 3 different prices over 45 days

Description

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason―to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Key Features

New Store Stock

Product Specifications

Brand: MIT Press
Colour: Navy
Format: hardcover
ASIN: 0262013193
Category: Books > Subjects > Science, Nature & Maths > Engineering & Technology > Education > Higher Education
Domain: Amazon UK
Release Date: 16 November 2009
Listed Since: 01 May 2009

Barcode

No barcode data available

MIT Press Probabilistic Graphical Models – Principles and Techniques

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning)

Decision Making with Imperfect Decision Makers: 28 (Intelligent Systems Reference Library, 28)

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning)

Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence, 858)

Machine Learning for Causal Inference

Practical Issues of Intelligent Innovations: 140 (Studies in Systems, Decision and Control, 140)

Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)

Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)

Inferential Models: Reasoning with Uncertainty (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Advances in the Theory and Practice of Statistics: A Volume in Honor of Samuel Kotz (Wiley Series in Probability and Statistics. Applied Probability and statistIcs)

Probability, Choice, and Reason

Machine Learning – A Probabilistic Perspective

Bayesian Programming (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Coding Ockham's Razor

Intelligence artificielle - 4e édition: Une approche moderne

Model-Based Machine Learning

Bayes Theory (Springer Series in Statistics)

Springer Bayesian Inference and MaxEnt Methods - MaxEnt 37

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining