Catálogo de publicaciones - libros
Conditionals, Information, and Inference: International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers
Gabriele Kern-Isberner ; Wilhelm Rödder ; Friedhelm Kulmann (eds.)
En conferencia: International Workshop on Conditionals, Information, and Inference (WCII) . Hagen, Germany . May 13, 2002 - May 15, 2002
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Artificial Intelligence; Mathematical Logic and Formal Languages
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-25332-7
ISBN electrónico
978-3-540-32235-1
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2005
Cobertura temática
Tabla de contenidos
doi: 10.1007/11408017_11
There Is a Reason for Everything (Probably): On the Application of Maxent to Induction
Jeff B. Paris; Alena Vencovská
In this paper we show how the maxent paradigm may be used to produce an inductive method (in the sense of Carnap) applicable to a wide class of problems in inductive logic. A surprising consequence of this method is that the answers it gives are consistent with, or explicable by, the existence of underlying reasons for the given knowledge base, even when no such reasons are explicitly present. We would conjecture that the same result holds for the full class of problems of this type.
- Regular Papers | Pp. 180-199
doi: 10.1007/11408017_12
Completing Incomplete Bayesian Networks
Manfred Schramm; Bertram Fronhöfer
For reasoning with uncertain knowledge the use of probability theory has been broadly investigated. Two main approaches have been developed: Bayesian Networks and MaxEnt Completion. In this paper we investigate ways to combine these two approaches: We consider two kinds of incomplete Bayesian Networks — thus coping with incomplete (uncertain) knowledge — and study the usefulness of some variations of the MaxEnt Completion for processing or completing them.
This analysis detected limits of the use of so-called update method .
- Regular Papers | Pp. 200-218