On Bias-Variance Analysis for Probabilistic Logic Models

On Bias-Variance Analysis for Probabilistic Logic Models

Huma Lodhi (Imperial College London, UK)
Copyright: © 2008 |Pages: 14
DOI: 10.4018/jitr.2008070103


The article introduces bias-variance decomposition in probabilistic logic learning. We use Stochastic Logic Programs for probabilistic logic representation. In order to learn probabilistic logic models we use Failure Adjusted Maximization (FAM) that is an instance of the Expectation Maximization (EM) algorithm for first order logic. Experiments are carried out by concentrating on one kind of application: quantitative modelling of metabolic pathways that is a complex and challenging task in computational systems biology. We apply bias-variance definitions to analyze quantitative modelling of amino acid pathways of Saccharomyces cerevisiae (yeast). The results show the phenomenon of bias-variance trade-off in probabilistic logic learning.

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