Name: Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition (en Inglés)
Price: 69.52 USD
Availability: InStock
Author: Dechter, Rina
ISBN: 9783031004551

portada Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition (en Inglés)

Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition (en Inglés)

Formato

Libro Físico

Autor

Rina Dechter

Editorial

Springer

Idioma

Inglés

N° páginas

185

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 1.1 cm

Peso

0.36 kg.

ISBN13

9783031004551

N° edición

Categorías

Probabilidad y estadística
Matemáticas aplicadas
Inteligencia artificial

Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition (en Inglés)

Rina Dechter (Autor) · Springer · Tapa Blanda

Libro Nuevo Importado

Envío: 16 a 23 días háb.

$ 115.86$ 69.52

-40%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 69.52

Llega entre el 03 Sep y el 16 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition (en Inglés)"

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.