Mixtures of truncated basis functions

Langseth, Helge; Nielsen, Thomas D.; Rumí, Rafael; Salmerón, Antonio

doi:10.1016/j.ijar.2011.10.004

Cited by 62 publications

(62 citation statements)

References 14 publications

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“…An important feature of the technique presented in this paper is that it can be directly applied using frameworks related to MTEs, like the Mixtures of Polynomials (MOPs) [32] and more generally, Mixtures of Truncated Basis Functions (MoTBFs) [19]. This can lead to improvements in inference efficiency, especially using MoTBFs, as they can provide accurate estimations with no need to split the domain of the densities [16].…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Data clustering using hidden variables in hybrid Bayesian networks

et al. 2014

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In this paper, we analyze the problem of data clustering in domains where discrete and continuous variables coexist. We propose the use of hybrid Bayesian networks with naïve Bayes structure and hidden class variable. The model integrates discrete and continuous features, by representing the conditional distributions as mixtures of truncated exponentials (MTEs). The number of classes is determined through an iterative procedure based on a variation of the data augmentation algorithm. The new model is compared with an EM-based clustering algorithm where each class model is a product of conditionally independent probability distributions and the number of clusters is decided by using a cross-validation scheme. Experiments carried out over real-world and synthetic data sets show that the proposal is competitive with state-of-the-art methods. Even though the methodology introduced in this manuscript is based on the use of MTEs, it can be easily instantiated to other similar models, like the Mixtures of Polynomials or the mixtures of truncated basis functions in general.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Data clustering using hidden variables in hybrid Bayesian networks

et al. 2014

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“…Our goal is to analyse the performance of probabilistic inference in hybrid Bayesian networks in scenarios where data come in streams at high speed, and therefore a quick response is required. Because of that, we will focus our analysis on conditional linear Gaussian (CLG) models [10,11], instead of more expressive alternatives such as mixtures of exponentials [12], mixtures of polynomials [18] and mixtures of truncated basis functions in general [9], as inference in the latter models is in general more time consuming [15].…”

Section: Introductionmentioning

confidence: 99%

Parallel Importance Sampling in Conditional Linear Gaussian Networks

Salmerón

Ramos-López

Borchani

et al. 2015

Advances in Artificial Intelligence

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Abstract. In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic networks show how a parallel version importance sampling, and more precisely evidence weighting, is a promising scheme, as it is accurate and scales up with respect to available computing resources.

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“…Mixtures of truncated basis functions (MoTBFs) [2] have recently been proposed as a general framework for handling hybrid Bayesian networks, i.e., Bayesian networks where discrete and continuous variables coexist. Previous hybrid models as the so-called mixtures of truncated exponentials (MTEs) [7] and mixtures of polynomials (MoPs) [10] can be regarded as particular cases of MoTBFs.…”

Section: Introductionmentioning

confidence: 99%

Learning Conditional Distributions Using Mixtures of Truncated Basis Functions

Pérez-Bernabé

Salmerón

Langseth

2015

Lecture Notes in Computer Science

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Abstract. 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 distributions. The main contribution of this work is conveyed through an empirical investigation into the properties of the new learning procedure, where we also compare the merits of our approach to those obtained by other proposals.

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Mixtures of truncated basis functions

Cited by 62 publications

References 14 publications

Data clustering using hidden variables in hybrid Bayesian networks

Data clustering using hidden variables in hybrid Bayesian networks

Parallel Importance Sampling in Conditional Linear Gaussian Networks

Learning Conditional Distributions Using Mixtures of Truncated Basis Functions

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