PortadaGruposCharlasMásPanorama actual
Buscar en el sitio
Este sitio utiliza cookies para ofrecer nuestros servicios, mejorar el rendimiento, análisis y (si no estás registrado) publicidad. Al usar LibraryThing reconoces que has leído y comprendido nuestros términos de servicio y política de privacidad. El uso del sitio y de los servicios está sujeto a estas políticas y términos.

Resultados de Google Books

Pulse en una miniatura para ir a Google Books.

Cargando...

Bayesian Networks and Decision Graphs

por Finn V. Jensen, Thomas D. Nielsen

MiembrosReseñasPopularidadValoración promediaConversaciones
33Ninguno727,194 (4)Ninguno
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. < give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book. Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark. Thomas D. Nielsen is an associate professor at the same department.… (más)
Añadido recientemente pordeano27, tdietterich, apsingh, Gr-grg, billga
Ninguno
Cargando...

Inscríbete en LibraryThing para averiguar si este libro te gustará.

Actualmente no hay Conversaciones sobre este libro.

Ninguna reseña
sin reseñas | añadir una reseña

» Añade otros autores (1 posible)

Nombre del autorRolTipo de autor¿Obra?Estado
Finn V. Jensenautor principaltodas las edicionescalculado
Nielsen, Thomas D.autor principaltodas las edicionesconfirmado
Debes iniciar sesión para editar los datos de Conocimiento Común.
Para más ayuda, consulta la página de ayuda de Conocimiento Común.
Título canónico
Información procedente del conocimiento común inglés. Edita para encontrar en tu idioma.
Título original
Títulos alternativos
Fecha de publicación original
Personas/Personajes
Lugares importantes
Acontecimientos importantes
Películas relacionadas
Epígrafe
Dedicatoria
Primeras palabras
Citas
Últimas palabras
Aviso de desambiguación
Editores de la editorial
Blurbistas
Idioma original
DDC/MDS Canónico
LCC canónico

Referencias a esta obra en fuentes externas.

Wikipedia en inglés

Ninguno

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. < give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book. Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark. Thomas D. Nielsen is an associate professor at the same department.

No se han encontrado descripciones de biblioteca.

Descripción del libro
Resumen Haiku

Debates activos

Ninguno

Cubiertas populares

Enlaces rápidos

Valoración

Promedio: (4)
0.5
1
1.5
2
2.5
3 1
3.5
4 2
4.5
5 1

¿Eres tú?

Conviértete en un Autor de LibraryThing.

 

Acerca de | Contactar | LibraryThing.com | Privacidad/Condiciones | Ayuda/Preguntas frecuentes | Blog | Tienda | APIs | TinyCat | Bibliotecas heredadas | Primeros reseñadores | Conocimiento común | 203,243,356 libros! | Barra superior: Siempre visible