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Sebastian Schmon

Sebastian Schmon

Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, High-Dimensional Statistics

I am a DPhil student at the Department of Statistics. My research interest are Bayesian Statistics and stochastic simulation techniques, in particular, the pseudo-marginal Metropolis-Hastings algorithm for intractable distributions.

My supervisors are Arnaud Doucet and George Deligiannidis.

Publications

2020

  • S. M. Schmon , G. Deligiannidis , A. Doucet , M. K. Pitt , Large-sample asymptotics of the pseudo-marginal method, Biometrika, Jul. 2020.
  • S. Schmon , A. Doucet , G. Deligiannidis , Bernoulli race particle filters, in AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 2020.
  • S. Schmon , G. Deligiannidis , A. Doucet , M. Pitt , Large sample asymptotics of the pseudo-marginal method, Biometrika, 2020.
  • T. Joy , S. M. Schmon , P. Torr , S. Narayanaswamy , T. Rainforth , Rethinking Semi–Supervised Learning in VAEs, https://arxiv.org/abs/2006.10102, 2020.
  • S. Groha , S. M. Schmon , A. Gusev , Neural ODEs for Multi-state Survival Analysis, https://arxiv.org/abs/2006.04893, 2020.
  • S. M. Schmon , P. W. Cannon , J. Knoblauch , Generalized Posteriors in Approximate Bayesian Computation. 2020.

2019

  • S. M. Schmon , G. Deligiannidis , A. Doucet , Bernoulli Race Particle Filters, AISTATS, 2019.
  • J. K. Fitzsimons , S. M. Schmon , S. J. Roberts , Implicit Priors for Knowledge Sharing in Bayesian Neural Networks, 4th Neurips workshop on Bayesian Deep Learning, 2019.