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SUMMARY:Multigrid Monte Carlo Revisited: Theory and Bayesian Inference
DTSTART:20240904T161500
DTEND:20240904T171500
DTSTAMP:20260601T073026Z
UID:4e1e0bea5dafd1fddba76d50af492a9d45e4b1901525d3be1bb5541a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Yoshihito Kazashi - University of Strathclyde Glasgow\nAbs
 tract:\n\nIn this talk\, I revisit the multigrid Monte Carlo (MGMC) method
  proposed by Goodman and Sokal [Goodman and Sokal\, (1989) Multigrid Monte
  Carlo method. Conceptual foundations]\, a random sampler analogue of dete
 rministic multigrid solvers.\n\n \n\nMGMC accelerates random samplers\, s
 uch as Gibbs samplers\, by drawing on insights from numerical analysis. Th
 e primary focus of this talk is to provide theoretical support. We discuss
  a grid-size-independent convergence theory for MGMC\, applicable to gener
 al Gaussian random variables. This theory demonstrates that the first two 
 moments\, which fully characterize the Gaussian distribution\, converge ex
 ponentially to their target values at a uniform rate. Additionally\, we ex
 amine the exponential decay of autocorrelations in the generated samples. 
 Furthermore\, we extend the application of the MGMC method to address the 
 important scenario of sampling posterior Gaussian distributions conditione
 d on noisy data.\n\n \n\nThis is joint work with Eike Müller (Bath\, UK)
  and Robert Scheichl (Heidelberg\, Germany)
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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