Mylène Bédard, Ph.D.

                                                                           Office:  4223, André-Aisenstadt building 

                                                                          Address:  Département de mathématiques et de statistique
                                                                                         Université de Montréal
                                                                                         P.O. Box 6128, succ. Centre-ville
                                                                                         Montréal, Québec, Canada
                                                                                         H3C 3J7

                                                                          Phone:  (514) 343-6111 poste 2727
                                                                          Fax: (514) 343-5700

                                                                          Email:  bedard@dms.umontreal.ca

            Assistant Professor

Version française

Curriculum vitae

Teaching

• STT 1995 - Analyses statistiques

• STT 2100 - Théorie de la décision

• STT 6115 - Théorie de la décision bayésienne

Research

    Published/Accepted Papers

• Bédard, M. (2007). Weak Convergence of Metropolis Algorithms for Non-iid Target Distributions. Ann. Appl. Probab. 17, 1222-44.    pdf / ps

• Bédard, M., Fraser, D.A.S., Wong, A. (2007). Higher Accuracy for Bayesian and Frequentist Inference: Large Sample Theory for Small Sample Likelihood. Statist. Sci. 22, 301-21.    pdf / ps

• Bédard, M. (2008). Efficient Sampling using Metropolis Algorithms: Applications of Optimal Scaling Results. J. Comput. Graph. Statist. 17, 312-32.    pdf / ps

• Bédard, M. (2008). Optimal Acceptance Rates for Metropolis Algorithms: Moving Beyond 0.234. Stochastic Process. Appl. 118, 2198-222.    pdf / ps

• Bédard, M. and Rosenthal, J.S. (2008). Optimal Scaling of Metropolis Algorithms: Heading Toward General Target Distributions. Canad. J. Statist. 36, 483-503.    pdf / ps 

• Bédard, M. and Fraser, D.A.S. (2009). On a Directionally Adjusted Metropolis-Hastings Algorithm. IJSS 9 (Special Issue) , 33-57.    pdf / ps 

• Bédard, M. (2006). On the Robustness of Optimal Scaling for Random Walk Metropolis Algorithms. Ph.D. Thesis. Department of Statistics, University of Toronto.    pdf / ps 

    Submitted Papers

• Bédard, M. (2009). On the optimal scaling problem of Metropolis algorithms for hierarchical target distributions.

• Bédard, M., Douc, R., and E. Moulines. Scaling analysis of multiple-try MCMC methods.

    Papers in preparation

• Bédard, M. Scaling the Metropolis-within-Gibbs algorithm in high dimensions.

• Bédard, M., Douc, R., and E. Moulines. Scaling analysis of delayed rejection MCMC methods.

• Bédard, M. and Kendall, W.S. Weak Convergence of RWM Algorithms using Dirichlet Forms.

Supervision

• Matei Mireuta (part-time M.Sc., since February 2008)

• Audrey Béliveau (undergrad NSERC project, April 2008 - October 2008)     final_report.pdf

• Vanessa Bergeron Laperrière (undergrad NSERC project, Mai 2010 - August 2010)     final_report.pdf

• Jean-François Bégin (undergrad NSERC project, Mai 2010 - August 2010)     final_report.pdf

• Zakaria Lounis (Ph.d., since September 2010)

• Assia Groiez (part-time M.Sc., since September 2010)

Last modified: 11-11-2008