Inference in Bayesian Networks with Recursive Probability Trees: Data Structure Definition and Operations

2013 ◽  
Vol 28 (7) ◽  
pp. 623-647 ◽  
Author(s):  
Andrés Cano ◽  
Manuel Gómez-Olmedo ◽  
Serafín Moral ◽  
Cora B. Pérez-Ariza ◽  
Antonio Salmerón
Keyword(s):  
Data Structure ◽  
Bayesian Networks ◽  
Probability Trees

scholarly journals Approximate inference in Bayesian networks using binary probability trees

2011 ◽  
Vol 52 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Andrés Cano ◽  
Manuel Gémez-Olmedo ◽  
Serafén Moral
Keyword(s):  
Bayesian Networks ◽  
Approximate Inference ◽  
Probability Trees

Recursive Probability Trees for Bayesian Networks

2010 ◽  
pp. 242-251 ◽  
Author(s):  
Andrés Cano ◽  
Manuel Gómez-Olmedo ◽  
Serafín Moral ◽  
Cora B. Pérez-Ariza
Keyword(s):  
Bayesian Networks ◽  
Probability Trees

scholarly journals FAST FACTORISATION OF PROBABILISTIC POTENTIALS AND ITS APPLICATION TO APPROXIMATE INFERENCE IN BAYESIAN NETWORKS

2012 ◽  
Vol 20 (02) ◽  
pp. 223-243 ◽  
Author(s):  
ANDRÉS CANO ◽  
MANUEL GÓMEZ-OLMEDO ◽  
CORA B. PÉREZ-ARIZA ◽  
ANTONIO SALMERÓN
Keyword(s):  
Bayesian Networks ◽  
Approximate Inference ◽  
Efficient Procedure ◽  
Inference Algorithms ◽  
Probability Trees

We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to carry out approximate factorisations guided by a parameter called factorisation degree, which is fast to compute. We show how this parameter can be used to control the tradeoff between complexity and accuracy in approximate inference algorithms for Bayesian networks.

Partial Abductive Inference in Bayesian Networks by Using Probability Trees

2004 ◽  
pp. 146-154 ◽  
Author(s):  
Luis M. de Campos ◽  
José A. Gámez ◽  
Serafín Moral
Keyword(s):  
Bayesian Networks ◽  
Abductive Inference ◽  
Probability Trees

scholarly journals Analysis of the Uncertainty in the relationship between Self-Determined Motivation and competitive Anxiety in dual-career students: Application of Information Theory and Bayesian Networks

2018 ◽  
Author(s):  
Xavier Ponseti ◽  
Pedro L. Almeida ◽  
Joao Lameiras ◽  
Bruno Martins ◽  
Aurelio Olmedilla-Zafra ◽  
...  
Keyword(s):  
Information Theory ◽  
Data Analysis ◽  
Intrinsic Motivation ◽  
Bayesian Networks ◽  
Dual Career ◽  
Psychological Variables ◽  
Competitive Anxiety ◽  
Psychosocial Studies ◽  
The Relationship ◽  
Probability Trees

This study is framed on the Information Theory as a constructive criterion to generate probabilistic distributions –through the elaboration of Bayesian Networks- and to reduce the uncertainty in the occurrence and relationship between two key psychological variables associated with the sports’ performance: Self-Determined Motivation and Competitive Anxiety. We analyzed 674 universitary students/athletes who competed in the 2017 Universitary Games (Universiade) in México, from 44 universities, with an average age of 21 years old (SD = 2.07), and with a sportive experience of 8.61 years of average (SD = 5.15). Methods: Regarding the data analysis, first of all a CHAID algorithm was carried out for to know the independence links among variables, and then two Bayesian networks (BN) were elaborated. The validation of the BN revealed AUC values ranging from 0.5 to 0.92. Subsequently, various instantations were carried out with hypothetical values applied to the “bottom” variables. Results showed two probability trees that have Extrinisic Motivation and Amotivation at the top, while the anxiety/activation due to the worry for performance was at the bottom of probabilities. The instantiations carried out support the existence of these probabilistic relationships, demonstrating the little influence on the competition anxiety generated by the intrinsic motivation. In conclusion, the reduction of the uncertainty made up by the restricted BN may aloe to re-introduce Information Theory principles in psychosocial studies, allowing authors to obtain useful probabilities values upon target psychological variables related with sportive performance.

scholarly journals Importance sampling in Bayesian networks using probability trees

2000 ◽  
Vol 34 (4) ◽  
pp. 387-413 ◽  
Author(s):  
Antonio Salmerón ◽  
Andrés Cano ◽  
Serafı́n Moral
Keyword(s):  
Bayesian Networks ◽  
Importance Sampling ◽  
Probability Trees

MPE Computation in Bayesian Networks Using Mini-Bucket and Probability Trees Approximation

2020 ◽  
Vol 28 (05) ◽  
pp. 785-805
Author(s):  
Andrés Cano ◽  
Manuel Gómez-Olmedo ◽  
Serafín Moral ◽  
Serafín Moral-García
Keyword(s):  
Bayesian Networks ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Approximation Procedure ◽  
Approximate Search ◽  
Probable Explanation ◽  
Deletion Algorithm ◽  
Most Probable Explanation ◽  
Search Approach ◽  
Probability Trees

Given a set of uncertain discrete variables with a joint probability distribution and a set of observations for some of them, the most probable explanation is a set or configuration of values for non-observed variables maximizing the conditional probability of these variables given the observations. This is a hard problem which can be solved by a deletion algorithm with max marginalization, having a complexity similar to the one of computing conditional probabilities. When this approach is unfeasible, an alternative is to carry out an approximate deletion algorithm, which can be used to guide the search of the most probable explanation, by using A* or branch and bound (the approximate+search approach). The most common approximation procedure has been the mini-bucket approach. In this paper it is shown that the use of probability trees as representation of potentials with a pruning of branches with similar values can improve the performance of this procedure. This is corroborated with an experimental study in which computation times are compared using randomly generated and benchmark Bayesian networks from UAI competitions.

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