GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion
نویسندگان
چکیده
منابع مشابه
Dynamic Bayesian Information Measures
This paper introduces measures of information for Bayesian analysis when the support of data distribution is truncated progressively. The focus is on the lifetime distributions where the support is truncated at the current age t>=0. Notions of uncertainty and information are presented and operationalized by Shannon entropy, Kullback-Leibler information, and mutual information. Dynamic updatings...
متن کاملMutual learning globally
This article is about the development of the trauma field over the last 20 years from an organizational perspective, and about trauma from a global, culture-sensitive perspective. My professional career is very closely linked to the development of the European Society for Traumatic Stress Studies (ESTSS) in the 1990s. Later on, I was fortunate enough to witness, and contribute to, the Internati...
متن کاملOptimal Models of Prosodic Prominence Using the Bayesian Information Criterion
This study investigated the relation between various acoustic features and prominence. Past research has suggested that duration, pitch, and intensity all play a role in the perception of prominence. In our past work, we found a correlation between these acoustic features and speaker agreement over the placement of prominence. The current study was motivated by a need to enrich our understandin...
متن کاملGlobally Optimal Structure Learning of Bayesian Networks from Data
The problem of finding a Bayesian network structure which maximizes a score function is known as Bayesian network structure learning from data. We study this problem in this paper with respect to a decomposable score function. Solving this problem is known to be NP-hard. Several algorithms are proposed to overcome this problem such as hill-climbing, dynamic programming, branch and bound, and so...
متن کاملLearning Bayesian Networks under the Control of Mutual Information
The extraction of the structure of a Bayesian network from data is conceived as a stochastic process of learning a random acyclic directed graph. The nodes of the graph are taken as xed but its arcs are randomly switched on or oo. The probabilities for the arcs being in the on or oo state are controlled by the amount and the precision of the mutual information the arcs transmit to the neighbori...
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: Bioinformatics
سال: 2011
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btr457