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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
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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 non-asymptotic 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 Metropolis-Hastings,” Under revision WIRE Computational Statistics, 2023 [paper]
- Austin Brown, “Geometric ergodicity of Gibbs samplers for Bayesian error-in-variable regression,” Electronic Journal of Statistics, 2024. [paper]
- Austin Brown and Galin L. Jones, “Lower bounds on the rate of convergence for accept-reject-based 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]
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Invited talks and posters
- Lower Bounds on the Rate of Convergence Metropolis-Hastings in Wasserstein Distances, B.B. Seminar, University of Toronto [slides]
- Lower Bounds on the Rate of Convergence Metropolis-Hastings in Wasserstein Distances, Statistics Departmental Seminar, University of Florida [slides]
- Lower Bounds on the Rate of Convergence for Accept-Reject-Based 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 Error-in-variable Regression, Bioinference 2023, Oxford University [poster]
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Statistics packages
- cmhi: a Python package for the centered Metropolis-Hastings independence algorithm. [Github link]
- mhlb: a Python implementation to estimate lower bounds on the geometric convergence rate for RWM Metropolis-Hastings. [Github link]
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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