Imperial College London > Talks@ee.imperial > CAS Talks > Markov Chain Monte Carlo using reconfigurable hardware

Markov Chain Monte Carlo using reconfigurable hardware

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If you have a question about this talk, please contact Grigorios Mingas.

Markov Chain Monte Carlo (MCMC) is a class of Monte Carlo algorithms used to sample from arbitrary probability distributions that occur in scientific simulation and Bayesian inference applications. When a distribution is multimodal and/or multidimensional, MCMC algorithms frequently become inefficient. In this presentation I will describe the most important issues in MCMC -related research, comment on existing MCMC samplers and share my thoughts on how we can use FPG As to accelerate these samplers. Main areas of interest include the parallelisation potential of MCMC and the effects of finite precision arithmetic on its sampling accuracy.

This talk is part of the CAS Talks series.

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