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(11 Apr 2011,
Date: 2011-04-15 Time: 11:00 Room: [[http://www.cs.uu.nl/docs/reach/bbl.php][BBL]] [[http://www.cs.uu.nl/info/plan/bbl.php]] ----+++++ Speaker: Silja Renooij ----+++++ Title: Sensitivity in Graphical Models: Bayesian Network Sensitivity Functions for Arc Removal and Hidden Markov Models ----+++++ Abstract Bayesian networks are a member of the family of graphical models that provide a graph-based representation for encoding a probability distribution over a multi-dimensional space. A Bayesian network combines a directed acyclic graph, representing the independences between variables, with discrete conditional probability distributions for each variable. A large number of probability parameters is required to completely specify the conditional probability distributions, and these are bound to be inaccurate. Sensitivity analysis is a general technique to study the effects of changes in network parameters on some output of interest. Our broad experience in sensitivity analysis for Bayesian networks had led to several new insights and inventions. In this presentation, I will demonstrate that analysing the effects of removing an arc from the graph of a Bayesian network can also be considered a type of sensitivity analysis. In addition, I will show that results stemming from sensitivity research in Bayesian networks apply to another member of the graphical model family: the Hidden Markov model; from these results we now have an efficient algorithm for sensitivity analysis in such models as well."
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