### Optimization-based Sampling Approaches for Bayesian Inference

Statistics Seminar

23rd November 2018, 3:30 pm – 4:30 pm

Main Maths Building, SM2

Markov chain Monte Carlo (MCMC) relies on efficient

proposals to sample from a target distribution of interest. Recent

optimization-based MCMC algorithms for Bayesian inference, e.g.,

randomize-then-optimize (RTO), repeatedly solve optimization problems

to obtain proposal samples. We interpret RTO as an invertible map

between two random functions and find that this mapping preserves the

random functions along many directions. This leads to a dimension

independent formulation of the RTO algorithm for sampling the

posterior of large-scale Bayesian inverse problems. We applied our new

methods on Hierarchical Bayesian inverse problems.

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