Can I read Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) on EtoBox?

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) by Daphne Koller and Nir Friedman is a science book available to read on EtoBox.

What is Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) about?

**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 corner

Who reads Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)?

Author

Daphne Koller and Nir Friedman

Publisher

The MIT Press

Published

2009

Language

EN

ISBN

9780262013192

Category

science

Subjects

Mathematics, Engineering, Reference

Rating

4.24 / 5 (147 ratings)

Updated

2026-03-14