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Mylène Bédard

Observed names: Bédard, Mylène, MYLÈNE BÉDARD
ORCID: 0000-0002-7372-9789
Observed ORCID: 0000-0002-7372-9789
OpenAlex ID: A5005486840
Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234
2008 · Mylène Bédard · Stochastic Processes and their Applications · 104
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2007), Weak convergence of Metropolis algorithms for non-iid target distributions
2006 · Mylène Bédard · 76
Optimal scaling of Metropolis algorithms: Heading toward general target distributions
2008 · Mylène Bédard, Jeffrey S. Rosenthal · Canadian Journal of Statistics · 62
Scaling analysis of multiple-try MCMC methods
2011 · Mylène Bédard, Randal Douc, et al. · Stochastic Processes and their Applications · 48
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A New Bayesian Approach to Robustness Against Outliers in Linear Regression
2019 · Philippe Gagnon, Alain Desgagné, et al. · Bayesian Analysis · 30
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Scaling Analysis of Delayed Rejection MCMC Methods
2013 · Mylène Bédard, Randal Douc, et al. · Methodology And Computing In Applied Probability · 22
Efficient Sampling Using Metropolis Algorithms: Applications of Optimal Scaling Results
2008 · Mylène Bédard · Journal of Computational and Graphical Statistics · 16
An automatic robust Bayesian approach to principal component regression
2020 · Philippe Gagnon, Mylène Bédard, et al. · Journal of Applied Statistics · 14
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Simulating from the Heston model: A gamma approximation scheme
2015 · Jean-François Bégin, Mylène Bédard, et al. · Monte Carlo Methods and Applications · 11
A Dirichlet form approach to MCMC optimal scaling
2017 · Giacomo Zanella, Mylène Bédard, et al. · Stochastic Processes and their Applications · 9
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On the robustness of optimal scaling for random walk metropolis algorithms
2006 · Mylène Bédard · TSpace (University of Toronto) · 9
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An adaptive multiple-try Metropolis algorithm
2022 · Simon Fontaine, Mylène Bédard · Bernoulli · 6
Weak convergence and optimal tuning of the reversible jump algorithm
2018 · Philippe Gagnon, Mylène Bédard, et al. · Mathematics and Computers in Simulation · 6
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Hierarchical Models and Tuning of Random Walk Metropolis Algorithms
2019 · Mylène Bédard · Journal of Probability and Statistics · 5
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Hierarchical models: Local proposal variances for RWM-within-Gibbs and MALA-within-Gibbs
2016 · Mylène Bédard · Computational Statistics & Data Analysis · 5
On the empirical efficiency of local MCMC algorithms with pools of proposals
2013 · Mylène Bédard, Matei Mireuta · Canadian Journal of Statistics · 3
MALA with annealed proposals: a generalization of locally and globally balanced proposal distributions
2021 · Gabriel Boisvert-Beaudry, Mylène Bédard · Statistics and Computing · 2
The linear Lasso: A location model approach
2022 · D. A. S. Fraser, Mylène Bédard · Canadian Journal of Statistics · 1
On‐line partitioning of the sample space in the regional adaptive algorithm
2020 · Nicolas Grenon‐Godbout, Mylène Bédard · Canadian Journal of Statistics · 1
An Efficient Bayesian Robust Principal Component Regression
2017 · Philippe Gagnon, Mylène Bédard, et al. · arXiv (Cornell University) · 1
Bayesian Robustness to Outliers in Linear Regression
2016 · Philippe Gagnon, Alain Desgagné, et al. · 1
Short Address To Gordon Alimentaire
2026 · Carrier, Julie, Lacelle, Nathalie, et al. · Zenodo (CERN European Organization for Nuclear Research)
Short Address To Mcgill Health Center
2026 · Carrier, Julie, Lacelle, Nathalie, et al. · Zenodo (CERN European Organization for Nuclear Research)
A short note on differential equations and on Geometry
2026 · Carrier, Julie, Lacelle, Nathalie, et al. · Zenodo (CERN European Organization for Nuclear Research)
Short Communication on Email Management Systems
2026 · Carrier, Julie, Lacelle, Nathalie, et al. · Zenodo (CERN European Organization for Nuclear Research)
Works count: 75
Citation count: 430
H-index: 9
I10-index: 9
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