Probabilistic Graphical Models: Principles and Techniques, Hardcover/Daphne Koller

Descriere YEO:

Probabilistic Graphical Models: Principles and - Disponibil la elefant.ro

Pe YEO găsești Probabilistic Graphical Models: Principles and de la Mit Press, în categoria Foreign Books.

Indiferent de nevoile tale, Probabilistic Graphical Models: Principles and Techniques, Hardcover/Daphne Koller din categoria Foreign Books îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.

Preț: 817.99 Lei

Caracteristicile produsului Probabilistic Graphical Models: Principles and

• Brand: Mit Press
• Categoria: Foreign Books
• Magazin: elefant.ro
• Ultima actualizare: 21-12-2024 01:38:29

Comandă Probabilistic Graphical Models: Principles and Online, Simplu și Rapid

Prin intermediul platformei YEO, poți comanda Probabilistic Graphical Models: Principles and de la elefant.ro rapid și în siguranță. Bucură-te de o experiență de cumpărături online optimizată și descoperă cele mai bune oferte actualizate constant.

Descriere magazin:
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. About the Author Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.