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SUMMARY:Weak Poincaré inequality comparisons for ideal and hybrid slice s
 ampling - Andi Wang (University of Warwick)
DTSTART:20241126T091500Z
DTEND:20241126T100500Z
UID:TALK221482@talks.cam.ac.uk
DESCRIPTION:Weak Poincar&eacute\; inequalities are a class of functional i
 nequality that can be used to give L2 convergence guarantees for ergodic M
 arkov chains. Notably\, they can be applied in situations where the chain 
 possesses no spectral gap\, and the convergence to equilibrium occurs at a
  subgeometric rate. They can also be used to compare Markov chains and the
 ir convergence rates\, often when one chain is an "approximate" version of
  some ideal chain. In this talk I will introduce this comparison framework
  based on weak Poincar&eacute\; inequalities\, and then apply them to comp
 are convergence rates of Hybrid versions of the popular MCMC algorithm kno
 wn as Slice Sampling to their ideal counterparts. Joint work with Sam Powe
 r\, Daniel Rudolf and Bj&ouml\;rn Sprungk.
LOCATION:Seminar Room 1\, Newton Institute
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