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Learning the structure of graphical models with latent variables

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If you have a question about this talk, please contact Florian Markowetz.

I will describe our work on the problem of learning the structure of probabilistic graphical models from data with hidden or missing variables. This general machine learning problem is applicable to gene regulatory network inference, which I will touch upon briefly. In particular I will review work in our group on (i) variational Bayesian learning of graph structures, (ii) inference of gene regulatory networks from state-space models of time series data, (iii) how to infer the number of latent variables, and (iv) Bayesian inference in directed mixed graphs.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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