Name: Introduction to Lifted Probabilistic Inference (Neural Information Processing Series) (en Inglés)
SKU: 53586634
Price: 100.38 USD
Availability: InStock
Author: Guy Van Den Broeck; Kristin Kersting

portada Introduction to Lifted Probabilistic Inference (Neural Information Processing Series) (en Inglés)

Introduction to Lifted Probabilistic Inference (Neural Information Processing Series) (en Inglés)

Formato

Libro Físico

Editorial

Mit Press

Autor

Guy Van Den Broeck; Kristin Kersting

Año

2021

Idioma

Inglés

N° páginas

454

Encuadernación

Tapa Blanda

ISBN13

9780262542593

Categorías

Inteligencia artificial

Introduction to Lifted Probabilistic Inference (Neural Information Processing Series) (en Inglés)

Guy Van Den Broeck; Kristin Kersting (Autor) · Mit Press · Tapa Blanda

Libro Nuevo Importado

Envío: 21 a 28 días háb.

$ 200.76$ 100.38

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 100 unidades

$ 100.38

Llega entre el 23 Sep y el 06 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Introduction to Lifted Probabilistic Inference (Neural Information Processing Series) (en Inglés)"

Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.