### Resumé

Originalsprog | Engelsk |
---|---|

Titel | Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings |

Redaktører | Sébastien Destercke, Thierry Denoeux |

Forlag | Springer |

Publikationsdato | 2015 |

Sider | 407-416 |

ISBN (Trykt) | 978-3-319-20806-0 |

ISBN (Elektronisk) | 978-3-319-20807-7 |

DOI | |

Status | Udgivet - 2015 |

Begivenhed | The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty - Compiegne, Frankrig Varighed: 15 jul. 2015 → 17 jul. 2015 |

### Konference

Konference | The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty |
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Land | Frankrig |

By | Compiegne |

Periode | 15/07/2015 → 17/07/2015 |

Navn | Lecture Notes in Computer Science |
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Nummer | 9161 |

ISSN | 0302-9743 |

### Fingerprint

### Citer dette

*Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings*(s. 407-416). Springer. Lecture Notes in Computer Science, Nr. 9161 https://doi.org/10.1007/978-3-319-20807-7_37

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*Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings.*Springer, Lecture Notes in Computer Science, nr. 9161, s. 407-416, The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Compiegne, Frankrig, 15/07/2015. https://doi.org/10.1007/978-3-319-20807-7_37

**MPE inference in conditional linear gaussian networks.** / Salmerón, Antonio; Rumí, Rafael; Langseth, Helge; Madsen, Anders Læsø; Nielsen, Thomas Dyhre.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - MPE inference in conditional linear gaussian networks

AU - Salmerón, Antonio

AU - Rumí, Rafael

AU - Langseth, Helge

AU - Madsen, Anders Læsø

AU - Nielsen, Thomas Dyhre

PY - 2015

Y1 - 2015

N2 - Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is the problem of finding a configuration of the remaining variables with maximum posterior probability. This problem has previously been addressed for discrete Bayesian networks and can be solved using inference methods similar to those used for finding posterior probabilities. However, when dealing with hybrid Bayesian networks, such as conditional linear Gaussian (CLG) networks, the MPE problem has only received little attention. In this paper, we provide insights into the general problem of finding an MPE configuration in a CLG network. For solving this problem, we devise an algorithm based on bucket elimination and with the same computational complexity as that of calculating posterior marginals in a CLG network. We illustrate the workings of the algorithm using a detailed numerical example, and discuss possible extensions of the algorithm for handling the more general problem of finding a maximum a posteriori hypothesis (MAP).

AB - Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is the problem of finding a configuration of the remaining variables with maximum posterior probability. This problem has previously been addressed for discrete Bayesian networks and can be solved using inference methods similar to those used for finding posterior probabilities. However, when dealing with hybrid Bayesian networks, such as conditional linear Gaussian (CLG) networks, the MPE problem has only received little attention. In this paper, we provide insights into the general problem of finding an MPE configuration in a CLG network. For solving this problem, we devise an algorithm based on bucket elimination and with the same computational complexity as that of calculating posterior marginals in a CLG network. We illustrate the workings of the algorithm using a detailed numerical example, and discuss possible extensions of the algorithm for handling the more general problem of finding a maximum a posteriori hypothesis (MAP).

U2 - 10.1007/978-3-319-20807-7_37

DO - 10.1007/978-3-319-20807-7_37

M3 - Article in proceeding

SN - 978-3-319-20806-0

SP - 407

EP - 416

BT - Symbolic and Quantitative Approaches to Reasoning with Uncertainty

A2 - Destercke, Sébastien

A2 - Denoeux, Thierry

PB - Springer

ER -