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Bédard, Mylène

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Full Professor

Faculty of Arts and Science - Department of Mathematics and Statistics

André-Aisenstadt Office 4223

514 343-6111 ext 2727

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Affiliations

  • Membre Centre de recherches mathématiques

Courses

  • ACT1240 A - Mathématiques financières

Research area

Research projects Expand all Collapse all

Centre de recherches mathématiques (CRM) FRQNT/Fonds de recherche du Québec - Nature et technologies (FQRNT) / 2022 - 2029

Supplément COVID-19 CRSNG_Markov chain Monte Carlo algorithms and locally informed proposal distributions CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2020 - 2021

Markov chain Monte Carlo algorithms and locally informed proposal distributions CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2019 - 2025

STUDYING, IMPROVING, AND APPLYING MARKOV CHAIN MONTE CARLO METHODS CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2014 - 2021

EFFICIENCY OF MARKOV CHAIN MONTE CARLO METHODS CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2008 - 2015

EFFICIENCY OF MARKOV CHAIN MONTE CARLO METHODS / 2008 - 2013

Selected publications Expand all Collapse all

On the empirical efficiency of local MCMC algorithms with pools of proposals

Bédard, Mylène et Mireuta, Matei, On the empirical efficiency of local MCMC algorithms with pools of proposals 41, 657--678 (2013), , Canad. J. Statist.

Scaling analysis of multiple-try MCMC methods

Bédard, Mylène, Douc, Randal et Moulines, Eric, Scaling analysis of multiple-try MCMC methods 122, 758--786 (2012), , Stochastic Process. Appl.

Simulating from the Heston model: A gamma approximation scheme

Bégin, Jean-François, Bédard, Mylène et Gaillardetz, P., Simulating from the Heston model: A gamma approximation scheme , 24 (2012), , Quantitative Finance

Scaling analysis of delayed rejection MCMC methods

Bédard, Mylène, Douc, Randal et Moulines, Eric, Scaling analysis of delayed rejection MCMC methods , 28 (2010), , Methodology & Computing in Applied Probability

On a directionally adjusted Metropolis-Hastings algorithm

Bédard, Mylène et Fraser, D.A.S. , On a directionally adjusted Metropolis-Hastings algorithm 9, 33-57 (2009), , International Journal of Statistical Sciences

Optimal scaling of Metropolis algorithms: heading toward general target distributions

Bédard, Mylène et Rosenthal, Jeffrey S., Optimal scaling of Metropolis algorithms: heading toward general target distributions 36, 483--503 (2008), , Canad. J. Statist.

Efficient sampling using Metropolis algorithms: applications of optimal scaling results

Bédard, Mylène, Efficient sampling using Metropolis algorithms: applications of optimal scaling results 17, 312--332 (2008), , J. Comput. Graph. Statist.

Optimal acceptance rates for Metropolis algorithms: moving beyond 0.234

Bédard, Mylène, Optimal acceptance rates for Metropolis algorithms: moving beyond 0.234 118, 2198--2222 (2008), , Stochastic Process. Appl.

Higher accuracy for Bayesian and frequentist inference: large sample theory for small sample likelihood

Bédard, M., Fraser, D. A. S. et Wong, A., Higher accuracy for Bayesian and frequentist inference: large sample theory for small sample likelihood 22, 301--321 (2007), , Statist. Sci.

Weak convergence of Metropolis algorithms for non-i.i.d. target distributions

Bédard, Mylène, Weak convergence of Metropolis algorithms for non-i.i.d. target distributions 17, 1222--1244 (2007), , Ann. Appl. Probab.

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