Aggregating Probabilistic Models of Complex Multi-State Systems
نویسندگان
چکیده
منابع مشابه
Aggregating Imprecise Probabilistic Knowledge
The problem of aggregating two or more sources of information containing knowledge about a same domain is considered. We propose an aggregation rule for the case where the available information is modeled by coherent lower previsions, corresponding to convex sets of probability mass functions. The consistency between aggregated beliefs and sources of information is discussed. A closed formula, ...
متن کاملAggregating Probabilistic XML
Les sources d’incertitude et d’imprécision des données sont nombreuses. Une manière de gérer cette incertitude est d’associer aux données des annotations probabilistes. De nombreux modèles de bases de données probabilistes ont ainsi été proposés, dans les cadres relationnel et semi-structuré. Ce dernier est particulièrement adapté à la gestion de données incertaines provenant de traitement auto...
متن کاملAggregating Learned Probabilistic Beliefs
We consider the task of aggregating beliefs of sev eral experts. We assume that these beliefs are rep resented as probability distributions. We argue that the evaluation of any aggregation technique depends on the semantic context of this task. We propose a framework, in which we assume that nature generates samples from a 'true' distribution and different experts form their beliefs based on ...
متن کاملProbabilistic Multi-Context Systems
The concept of contexts is widely used in artificial intelligence. Several recent attempts have been made to formalize multi-context systems (MCS) for ontology applications. However, these approaches are unable to handle probabilistic knowledge. This paper introduces a formal framework for representing and reasoning about uncertainty in multi-context systems (called p-MCS). Some important prope...
متن کاملProbabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex timeseries data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalabl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2015
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2015.06.071