Efficient gradient computation for conditional Gaussian graphical models
First Claim
1. A system that facilitates statistical modeling, comprising a gradient determination component that utilizes a data set for a set of variables and probabilistic inference to determine parameter gradients for a log-likelihood of a conditional Gaussian (CG) graphical model over those variables with at least one continuous variable and with incomplete observation data for at least one of the variables.
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Abstract
The subject invention leverages standard probabilistic inference techniques to determine a log-likelihood for a conditional Gaussian graphical model of a data set with at least one continuous variable and with data not observed for at least one of the variables. This provides an efficient means to compute gradients for CG models with continuous variables and incomplete data observations. The subject invention allows gradient-based optimization processes to employ gradients to iteratively adapt parameters of models in order to improve incomplete data log-likelihoods and identify maximum likelihood estimates (MLE) and/or local maxima of the incomplete data log-likelihoods. Conditional Gaussian local gradients along with conditional multinomial local gradients determined by the subject invention can be utilized to facilitate in providing parameter gradients for full conditional Gaussian models.
60 Citations
32 Claims
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1. A system that facilitates statistical modeling, comprising
a gradient determination component that utilizes a data set for a set of variables and probabilistic inference to determine parameter gradients for a log-likelihood of a conditional Gaussian (CG) graphical model over those variables with at least one continuous variable and with incomplete observation data for at least one of the variables.
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13. A method for facilitating statistical modeling, comprising:
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receiving at least one data set with incomplete observation data for a set of variables with at least one continuous variable; and
utilizing the data set and probabilistic inference to determine parameter gradients for a log-likelihood of a conditional Gaussian (CG) graphical model over those variables. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 31)
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28. A system that facilitates statistical modeling, comprising:
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means for receiving at least one data set containing a set of variables; and
means for utilizing the data set for the set of variables and probabilistic inference to determine parameter gradients for a log-likelihood of a conditional Gaussian (CG) graphical model over those variables with at least one continuous variable and with incomplete observation data for at least one of the variables.
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29. A data packet, transmitted between two or more computer components, that facilitates statistical modeling, the data packet comprising, at least in part, information relating to a gradient determination system that determines gradients for a log-likelihood of a conditional Gaussian graphical model from a data set for a set of variables and probabilistic inference over those variables with at least one continuous variable and with incomplete observation data for at least one of the variables.
Specification