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Bédard, Mylène - Département de Mathématiques et Statistique, Université de Montréal
Efficient Sampling Using Metropolis Algorithms: Applications of Optimal Scaling Results
Weak Convergence of Metropolis Algorithms for Noniid Target Distributions
On a Directionally Adjusted Metropolis-Hastings Algorithm Myl`ene Bedard
ON THE ROBUSTNESS OF OPTIMAL SCALING FOR RANDOM WALK METROPOLIS ALGORITHMS
The Canadian Journal of Statistics Vol. 34, No. 4, 2006, Pages ???-???
Higher accuracy for Bayesian and frequentist inference: Large sample theory for small sample likelihood
MYLENE BEDARD, Ph.D. CONTACT INFORMATION
Weak Convergence of Metropolis Algorithms for Non-iid Target Distributions
Higher accuracy for Bayesian and frequentist inference: Large sample theory for small sample likelihood
Optimal Acceptance Rates for Metropolis Algorithms: Moving Beyond 0.234
ON THE ROBUSTNESS OF OPTIMAL SCALING FOR RANDOM WALK METROPOLIS ALGORITHMS
E#cient Sampling Using Metropolis Algorithms: Applications of Optimal Scaling Results
The Canadian Journal of Statistics Vol. 34, No. 4, 2006, Pages ??????
