
A copula transformation in multivariate mixed discretecontinuous models
Copulas allow a flexible and simultaneous modeling of complicated depend...
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Sufficiency, Separability and Temporal Probabilistic Models
Suppose we are given the conditional probability of one variable given s...
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Joint and conditional estimation of tagging and parsing models
This paper compares two different ways of estimating statistical languag...
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Identifying the Relevant Nodes Without Learning the Model
We propose a method to identify all the nodes that are relevant to compu...
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Coupled Generative Adversarial Networks
We propose coupled generative adversarial network (CoGAN) for learning a...
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On compatibility/incompatibility of two discrete probability distributions in the presence of incomplete specification
Conditional specification of distributions is a developing area with man...
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Estimating differential entropy using recursive copula splitting
A method for estimating the Shannon differential entropy of multidimensi...
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Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from MixedVariable Data: Method and Applications
The ability to estimate joint, conditional and marginal probability distributions over some set of variables is of great utility for many common machine learning tasks. However, estimating these distributions can be challenging, particularly in the case of data containing a mix of discrete and continuous variables. This paper presents a nonparametric method for estimating these distributions directly from a dataset. The data are first represented as a graph consisting of object nodes and attribute value nodes. Depending on the distribution to be estimated, an appropriate eigenvector equation is then constructed. This equation is then solved to find the corresponding stationary distribution of the graph, from which the required distributions can then be estimated and sampled from. The paper demonstrates how the method can be applied to many common machine learning tasks including classification, regression, missing value imputation, outlier detection, random vector generation, and clustering.
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