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Kernel Stein discrepancy minimization for MCMC thinning in cardiac electrophysiology

  • 29 Aug 2022
  • 4:00 PM - 5:00 PM (AEST)
  • Online


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The Bayesian Section of the SSA  invites you to a webinar by Dr Marina Riabiz, "Kernel Stein discrepancy minimization for MCMC thinning in cardiac electrophysiology."

Calcium is the end-point intracellular signal driving cardiac myocyte contraction, and its dynamic is described through coupled ordinary differential equations (ODEs). Markov Chain Monte Carlo (MCMC) can be used to characterize the posterior distribution of the parameters of the cardiac ODEs, which can then serve as an experimental design for multi-scale models of the whole hearth. However, MCMC suffers from poor mixing in high-dimensional settings, so post-processing of the MCMC output is required. Existing heuristics to assess the convergence and compress the MCMC output can produce sub-optimal empirical approximations, that suffer from bias-variance trade-offs if the length of the MCMC output is fixed. In this talk, I will present a novel method that retrospectively selects a subset of states, of fixed cardinality, from the sample path, such that the approximation provided by their empirical distribution is close to optimal. This is based on greedy minimisation of a kernel Stein discrepancy, and it is suitable when the gradient of the log-target can be evaluated and an approximation using a small number of states is required. Theoretical results guarantee consistency of the method and I will demonstrate its effectiveness in the cardiac electrophysiology problem at hand, together with interesting biological findings. 



Dr. Marina Riabiz received her undergraduate and master’s degree in Mathematical Engineering from Politecnico di Milano, Italy, specialising in Applied Statistics. She completed her PhD in the Signal Processing Group, Information Engineering, at the University of Cambridge, UK, working on latent variable models for Bayesian inference with stable distribution and processes. She joined King’s College London in 2018 as a postdoctoral researcher in the Cardiac Electro-Mechanics Research Group (BMEIS), working on uncertainty quantification for cardiac myocyte models. During this time, she was also a visiting researcher at the Alan Turing Institute. In September 2021 Marina joined the Department of Mathematics at King's College London as a Lecturer in Statistics.
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