Unified Conditional Probability Density functions for hybrid Bayesian networks
سال
: 2012
چکیده: Bayesian Network is a significant graphical model that is used to do probabilistic inference and reasoning under
uncertainty circumstances. In many applications, existence of discrete and continuous variables in the model are inevitable which has lead to high amount of researches on hybrid Bayesian networks in the recent years. Nevertheless, one of the challenges in inference in hybrid BNs is the difference between conditional probability density functions of different types of variables. In this paper, we propose an approach to construct a Unified Conditional Probability Density function (UCPD) that can represent probability distribution for both types of variables. No limitation is considered in the topology of the network. Hence, the construction of the unified CPD is developed for all pairs of nodes. We take use from mixture of
Gaussians in the UCPD construct. Additionally, we utilize Kullback–Liebler divergence to measure the accuracy of our estimations.
uncertainty circumstances. In many applications, existence of discrete and continuous variables in the model are inevitable which has lead to high amount of researches on hybrid Bayesian networks in the recent years. Nevertheless, one of the challenges in inference in hybrid BNs is the difference between conditional probability density functions of different types of variables. In this paper, we propose an approach to construct a Unified Conditional Probability Density function (UCPD) that can represent probability distribution for both types of variables. No limitation is considered in the topology of the network. Hence, the construction of the unified CPD is developed for all pairs of nodes. We take use from mixture of
Gaussians in the UCPD construct. Additionally, we utilize Kullback–Liebler divergence to measure the accuracy of our estimations.
کلیدواژه(گان): hybrid bayesian network,mixture of Gaussians,unified conditional probability density function
کالکشن
:
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آمار بازدید
Unified Conditional Probability Density functions for hybrid Bayesian networks
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contributor author | محدثه دلاوریان | en |
contributor author | محمود نقیب زاده | en |
contributor author | مهدی عمادی | en |
contributor author | Mohadeseh Delavarian | fa |
contributor author | Mahmoud Naghibzadeh | fa |
date accessioned | 2020-06-06T14:07:52Z | |
date available | 2020-06-06T14:07:52Z | |
date copyright | 8/14/2012 | |
date issued | 2012 | |
identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3383643 | |
description abstract | Bayesian Network is a significant graphical model that is used to do probabilistic inference and reasoning under uncertainty circumstances. In many applications, existence of discrete and continuous variables in the model are inevitable which has lead to high amount of researches on hybrid Bayesian networks in the recent years. Nevertheless, one of the challenges in inference in hybrid BNs is the difference between conditional probability density functions of different types of variables. In this paper, we propose an approach to construct a Unified Conditional Probability Density function (UCPD) that can represent probability distribution for both types of variables. No limitation is considered in the topology of the network. Hence, the construction of the unified CPD is developed for all pairs of nodes. We take use from mixture of Gaussians in the UCPD construct. Additionally, we utilize Kullback–Liebler divergence to measure the accuracy of our estimations. | en |
language | English | |
title | Unified Conditional Probability Density functions for hybrid Bayesian networks | en |
type | Conference Paper | |
contenttype | External Fulltext | |
subject keywords | hybrid bayesian network | en |
subject keywords | mixture of Gaussians | en |
subject keywords | unified conditional probability density function | en |
identifier link | https://profdoc.um.ac.ir/paper-abstract-1030835.html | |
conference title | International Conference on Uncertainty Reasoning and Knowledge Engineering | en |
conference location | جاکارتا | fa |
identifier articleid | 1030835 |