Alex Shestopaloff

I am a Fellow of the Alan Turing Institute, mentored by Arnaud Doucet and Ruth King. My interests are in developing efficient Monte Carlo methods for statistical inference problems.

Previously, I graduated with a PhD in Statistics from the Department of Statistical Sciences, University of Toronto in June 2016, where my supervisor was Radford M. Neal.

E-mail: ashestopaloff@turing.ac.uk

Preprints:

  • A.Y. Shestopaloff and R. M. Neal. MCMC for non-linear state space models using ensembles of latent sequences. Also available at arxiv.org
  • A.Y. Shestopaloff and R. M. Neal. On Bayesian inference for the M/G/1 queue with efficient MCMC sampling. Also available at arxiv.org
  • A.Y. Shestopaloff and R. M. Neal. Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods. Also available at arxiv.org

    Articles:

  • A.Y. Shestopaloff and R. M. Neal (2017). Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method. Bayesian Analysis. Available here. This is a revised version of the following technical report.

    Previously, I also worked on problems in investment performance measurement.

  • Y. Shestopaloff and A. Shestopaloff. Choosing the Right Solution of IRR Equation to Measure Investment Success. The Journal of Performance Measurement. Fall 2013.