منابع مشابه
Mixtures of truncated basis functions
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the mixture of polynomials (MoPs) framework. Similar to MTEs and MoPs, MoTBFs are defined so that the potentials are closed under combination and marginal...
متن کاملLearning Mixtures of Truncated Basis Functions from Data
In this paper we describe a new method for learning hybrid Bayesian network models from data. The method utilizes a kernel density estimator, which is in turn “translated” into a mixture of truncated basis functions-representation using a convex optimization technique. We argue that these estimators approximate the maximum likelihood estimators, and compare our approach to previous attempts at ...
متن کاملLearning Conditional Distributions Using Mixtures of Truncated Basis Functions
Mixtures of Truncated Basis Functions (MoTBFs) have recently been proposed for modelling univariate and joint distributions in hybrid Bayesian networks. In this paper we analyse the problem of learning conditional MoTBF distributions from data. Our approach utilizes a new technique for learning joint MoTBF densities, then propose a method for using these to generate the conditional distribution...
متن کاملIncorporating Prior Knowledge when Learning Mixtures of Truncated Basis Functions from Data
A quick recall of how of how to do approximations in R n : 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 We want to approximate the vector f = (3, 2, 5) with A vector along e 1 = (1, 0, 0). A quick recall of how of how to do approximations in R n : 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 We want to approximate the vector f = (3, 2, 5) with A vector along e 1 = (1, 0, 0). Best choice is f , e 1 · e 1 = (3, 0,...
متن کاملParameter Estimation in Mixtures of Truncated Exponentials
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains. On the other hand, estimating an MTE from data has turned out to be a difficult task, and most prevalent learning methods treat parameter estimation as a regression problem. The drawback of this approach is that by not directly attempting...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2012
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2011.10.004