Modern Bayesian Nonparametrics: beyond Dirichlet and Gaussian processes
published: Jan. 16, 2013, recorded: December 2012, views: 8766
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Nonparametrics plays an important role in Bayesian modelling: nonparametric models are flexible, realistic and by providing good coverage can guard against model inadequacy. Modern Bayesian nonparametrics builds on decades of research on Dirichlet and Gaussian processes to develop new models for complex data sources. I will briefly cover some examples of our recent work in this area: models for overlapping clustering and sparse arrays, probabilistic models of social and biological networks, diffusion tree models for hierarchical clustering, and models for covariance and volatility based on copulas and generalised Wishart processes. I will end on some discussion of limitations, links to classical nonparametrics, and directions for theory.
Download slides: nipsworkshops2012_ghahramani_bayesian_01.pdf (4.0 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !