| Astral International Pvt. Ltd.

260,900원  | 20250101  | 9781787153783

Foundations of Probabilistic Graphical Models: Provide a comprehensive introduction to probabilistic graphical models (PGMs), including their purpose, fundamental concepts, and types (e.g., Bayesian networks, Markov networks). Discuss the representation of complex probability distributions and dependencies using graphical structures.Graph Theory and Probability Foundations: Explore the underlying graph theory and probability principles essential for understanding PGMs.

 | Springer Verlag

115,740원  | 20161009  | 9781447170549

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.

Koller, Daphne  | MIT Press

194,420원  | 20091116  | 9780262013192

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.

 | Content Technologies, Inc.

0원  | 20101213  | 9781617440069

Never HIGHLIGHT a Book Again! Includes all testable terms, concepts, persons, places, and events. Cram101 Just the FACTS101 studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanies: 9780470722107. This item is printed on demand.

Drton, Mathias (EDT), Lauritzen, Steffen (EDT), Maathuis, Marloes (EDT), Wainwright, Martin (EDT)  | CRC Press

311,850원  | 20181127  | 9781498788625

Trail Mix: Wit & Wisdom from the Outdoors is a collection of quotes, poetry, and passages from classic books that provide outdoor inspiration to those in the woods, on the mountain, beside the water, or at home.

 | Kluwer

0원  | 19980401  | 9780792350170

 | CRC Press

118,030원  | 20241008  | 9781032039503

This book presents recent advancements in research, new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models. The book undertakes to stimulate scientific exchange, ideas, and experiences in the field of DSS applications.

Roverato, Alberto  | Cambridge University Press

42,260원  | 20170831  | 9781108404969

For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.

 | Now Publishers

181,910원  | 20191210  | 9781680836387

Discrete Graphical Models ? An Optimization Perspective is about discrete energy minimization for discrete graphical models. It considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their place in examples and remarks only.

Lucas, Peter (EDT)/ Gamez, Jose A. (EDT)/ Salmeron  | Springer

0원  | 20070504  | 9783540689942

In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence; contributions to the area are coming from computer science, mathematics, statistics and engineering. This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.

Whittaker, Joe  | John Wiley & Sons

161,140원  | 20111017  | 9780470743669

- It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.

 | Springer Nature B.V.

73,480원  | 20141009  | 9783319122908

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014.

Ankur Ankan, Abinash Panda  | Packt Publishing

32,000원  | 20151107  | 9781784394684

Xiang, Yang  | Cambridge

271,950원  | 20210101  | 9780521813082

This 2002 book identifies the technical challenges in building intelligent agents that can cooperate on complex tasks in an uncertain environment and provides a rigorous framework for meeting these challenges. It is a comprehensive book that addresses the subject of probabilistic inference by multiple agents using graphical knowledge representations.

Borgelt, Christian  | Wiley

231,960원  | 20210101  | 9780470722107

Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data All basic concepts carefully explained and illustrated by examples throughout Contains background material including graphical representation, including Markov and Bayesian Networks. Includes a comprehensive bibliography.