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SUMMARY:Skew-symmetric schemes for robust sampling from diffusions - Giorg
 os Vasdekis (Newcastle University)
DTSTART:20241111T140000Z
DTEND:20241111T150000Z
UID:TALK223867@talks.cam.ac.uk
DESCRIPTION:Locally balancing algorithms are a new class of MCMC algorithm
 s\, recently introduced in (Livingstone and Zanella\, 2022). One of these 
 algorithms\, the Barker algorithm\, has been shown to be robust to heteros
 kedasticity of the posterior target and the step size of the algorithm. At
  the same time\, the algorithm seems to preserve high dimensional properti
 es of state-of-the-art MCMC\, making it an interesting alternative to the 
 existing literature. It turns out that in order to sample from the Barker 
 algorithm\, one can use ideas of sampling from skew-symmetric distribution
 s. We will transfer these ideas in the context of (approximately) simulati
 ng from diffusion processes and we will suggest a new class of unadjusted 
 MCMC algorithms\, which seem robust with respect to the step size.\nThis i
 s joint work with S. Livingstone\, N. Nusken and R. Zhang.\n&nbsp\;
LOCATION:Seminar Room 2\, Newton Institute
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