
Contact
Email: ad.brown [at] utoronto [dot] ca
Department of Statistical Sciences at University of Toronto
Office 9183, 700 University Avenue, 9th Floor
Toronto, ON M5G 1Z5
Department of Statistical Sciences at University of Toronto
Office 9183, 700 University Avenue, 9th Floor
Toronto, ON M5G 1Z5

Publications and preprints
 Sabrina Sixta and Jeffrey S. Rosenthal and Austin Brown, “Bounding and estimating MCMC convergence rates using common random number simulations,” preprint, 2024 [paper]
 Austin Brown, “A nonasymptotic error analysis for parallel Monte Carlo estimation from many short Markov chains,” preprint, 2024 [paper]
 Austin Brown and Galin L. Jones, “A survey of modern convergence analysis for MetropolisHastings,” Under revision WIRE Computational Statistics, 2023 [paper]
 Austin Brown, “Geometric ergodicity of Gibbs samplers for Bayesian errorinvariable regression,” Electronic Journal of Statistics, 2024. [paper]
 Austin Brown and Galin L. Jones, “Lower bounds on the rate of convergence for acceptrejectbased Markov chains,” preprint, 2022. [paper]
 Austin Brown and Galin L. Jones (2024). Exact convergence analysis for Metropolis–Hastings independence samplers in Wasserstein distances. Journal of Applied Probability, 2023. [paper]

Invited talks and posters
 Lower Bounds on the Rate of Convergence MetropolisHastings in Wasserstein Distances, B.B. Seminar, University of Toronto [slides]
 Lower Bounds on the Rate of Convergence MetropolisHastings in Wasserstein Distances, Statistics Departmental Seminar, University of Florida [slides]
 Lower Bounds on the Rate of Convergence for AcceptRejectBased Markov Chains, Algorithms and Computationally Intensive Inference seminars, University of Warwick [slides]
 Exact convergence for independence samplers in Wasserstein distance, University of Warwick Departmental Conference 2023 [slides]
 Geometric Ergodicity of Gibbs Samplers for Bayesian Errorinvariable Regression, Bioinference 2023, Oxford University [poster]

Statistics packages
 cmhi: a Python package for the centered MetropolisHastings independence algorithm. [Github link]
 mhlb: a Python implementation to estimate lower bounds on the geometric convergence rate for RWM MetropolisHastings. [Github link]

Teaching
 Methods of Data Analysis 1 (STA302), University of Toronto, Fall 2023
 Introduction to Statistical Analysis (STAT 3011), University of Minnesota, Spring 2021
 Regression and Correlated Data (STAT 3032), University of Minnesota, Spring 2020