
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.
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.
Email: alexander@utstat.toronto.edu
Preprints:
A.Y. Shestopaloff and R. M. Neal.
MCMC for nonlinear 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 (2018).
Sampling latent states for highdimensional nonlinear state space models with the embedded HMM method. Bayesian Analysis. Available here. This is a revised version of the following technical report.
Miasnikof, P., Shestopaloff, A., Bonner, A. J. and Lawryshyn, Y. (2018). A Statistical Performance Analysis of
Graph Clustering Algorithms. In A. Bonato, P. Pralat and A. Raigorodskii (Eds.), Algorithms and Models for the
Web Graph: 15th International Workshop, WAW 2018, Moscow, Russia, May 1718, 2018, Proceedings (Lecture
Notes in Computer Science; Vol. 10836). Springer Nature.
Miasnikof, P., Giannakeas, V., Gomes, M., Aleksandrowicz, L., Shestopaloff, A. Y., Alam, D., Tollman, S.,
Samarikhalaj, A. and Jha, P. (2015) Naive Bayes classiers for verbal autopsies: comparison to physicianbased
classication for 21,000 child and adult deaths, BMC Medicine, 13:286.
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.
