
Hybrid Bayesian Networks with Linear Deterministic Variables
When a hybrid Bayesian network has conditionally deterministic variables...
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Interpolating Conditional Density Trees
Joint distributions over many variables are frequently modeled by decomp...
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Estimating WellPerforming Bayesian Networks using Bernoulli Mixtures
A novel method for estimating Bayesian network (BN) parameters from data...
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Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations
Score based learning (SBL) is a promising approach for learning Bayesian...
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Generalizing Tree Probability Estimation via Bayesian Networks
Probability estimation is one of the fundamental tasks in statistics and...
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An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length
Bayesian networks are convenient graphical expressions for high dimensio...
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The FEDHC Bayesian network learning algorithm
The paper proposes a new hybrid Bayesian network learning algorithm, ter...
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Mixnets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Continuous And Discrete Variables
Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in lowdimensional continuous space. In particular, mixtures of Gaussians can be fitted to data very quickly using an accelerated EM algorithm that employs multiresolution kdtrees (Moore, 1999). In this paper, we propose a kind of Bayesian networks in which lowdimensional mixtures of Gaussians over different subsets of the domain's variables are combined into a coherent joint probability model over the entire domain. The network is also capable of modeling complex dependencies between discrete variables and continuous variables without requiring discretization of the continuous variables. We present efficient heuristic algorithms for automatically learning these networks from data, and perform comparative experiments illustrated how well these networks model real scientific data and synthetic data. We also briefly discuss some possible improvements to the networks, as well as possible applications.
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