Knwoledge revision in Markov networks
A lot of research in graphical models has been devoted to developing correct and eficient evidence propagation methods, like join tree propagation or bucket elimination. With these methods it is possible to condition the represented probability distribution on given evidence, a reasoning process tha...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2004 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3640 |
| Acceso en línea: | https://hdl.handle.net/2099/3640 |
| Access Level: | acceso abierto |
| Palabra clave: | Markov networks Intel·ligència artificial Processos de Markov Classificació AMS::68 Computer science::68T Artificial intelligence |
| Sumario: | A lot of research in graphical models has been devoted to developing correct and eficient evidence propagation methods, like join tree propagation or bucket elimination. With these methods it is possible to condition the represented probability distribution on given evidence, a reasoning process that is sometimes also called focusing. In practice, however, there is the additional need to revise the represented probability distribution in order to reflect some knowledge changes by satisfying new frame conditions. Pure evidence propagation methods, as implemented in the known commercial tools for graphical models, are unsuited for this task. In this paper we develop a consistent scheme for the important task of revising a Markov network so that it satisfies given (conditional) marginal distributions for some of the variables. This task is of high practical relevance as we demonstrate with a complex application for item planning and capacity management in the automotive industry at Volkswagen Group. |
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