Stein’s method for multivariate distributions, kernelized goodness of fit statistics, and exponential random graph models

Gesine Reinert (Oxford University)

15-Oct-2020, 07:00-08:00 (3 years ago)

Abstract: Assessing the goodness of fit of models with continuous distributions for which the likelihood cannot be evaluated directly can be tackled using kernels which are based on Stein’s method for continuous multivariate distributions. Often independent replicas are assumed for this method. When the data are given in the form of a network, usually there is only one network available. If the data are hypothesised to come from an exponential random graph model, the likelihood cannot be calculated explicitly. Using a Stein operator for these models we introduce a kernelized goodness of fit test and illustrate its performance. This talk is based on joint work with Guillaume Mijoule, Nathan Ross, Yvik Swan and Wenkai Xu.

probability

Audience: researchers in the topic


Probability Victoria Seminar (PVSeminar)

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