University of Cambridge > Talks.cam > MRC Biostatistics Unit Seminars > BSU Seminar: 'Some advances and applications of robust gradient-based Markov chain Monte Carlo'

BSU Seminar: 'Some advances and applications of robust gradient-based Markov chain Monte Carlo'

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If you have a question about this talk, please contact Alison Quenault.

This will be a free hybrid seminar. To register to attend virtually, please click here: https://us02web.zoom.us/meeting/register/tZUsdOypqTwrH9LeiEEIL8dXxO_IpOC8lVMS

I will discuss some recent work on gradient-based Markov chain Monte Carlo. At their best gradient-based algorithms are often state-of-the-art, but they can also behave erratically in various ways. In the first part of the talk I’ll introduce a gradient-based scheme called the Barker proposal that can often match the performance of competitors such as MALA and HMC but is probably more robust. I will in particular highlight the benefits this brings in the context of adaptive MCMC . If time permits, in the second part of the talk I will talk about ongoing using MCMC for automatic model selection in poly-hazard models. The first part is joint work with Giacomo Zanella, Jure Vogrinc & Max Hird, the second is joint with Luke Hardcastle and Gianluca Baio.

This talk is part of the MRC Biostatistics Unit Seminars series.

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