Zobrazeno 1 - 10
of 3 300
pro vyhledávání: '"Rosenthal, A S"'
For random-walk Metropolis (RWM) and parallel tempering (PT) algorithms, an asymptotic acceptance rate of around 0.234 is known to be optimal in the high-dimensional limit. However, its practical relevance is uncertain due to restrictive derivation c
Externí odkaz:
http://arxiv.org/abs/2408.06894
We review and provide new proofs of results used to compare the efficiency of estimates generated by reversible MCMC algorithms on a general state space. We provide a full proof of the formula for the asymptotic variance for real-valued functionals o
Externí odkaz:
http://arxiv.org/abs/2408.04155
Autor:
Brown, Austin, Rosenthal, Jeffrey S.
This article develops general conditions for weak convergence of adaptive Markov chain Monte Carlo processes and is shown to imply a weak law of large numbers for bounded Lipschitz continuous functions. This allows an estimation theory for adaptive M
Externí odkaz:
http://arxiv.org/abs/2406.00820
Autor:
Liu, Darren, Ding, Cheng, Bold, Delgersuren, Bouvier, Monique, Lu, Jiaying, Shickel, Benjamin, Jabaley, Craig S., Zhang, Wenhui, Park, Soojin, Young, Michael J., Wainwright, Mark S., Clermont, Gilles, Rashidi, Parisa, Rosenthal, Eric S., Dimisko, Laurie, Xiao, Ran, Yoon, Joo Heung, Yang, Carl, Hu, Xiao
The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance. However, their performance in actual clinical applications has been underexplored. Traditional evaluations based on qu
Externí odkaz:
http://arxiv.org/abs/2401.13588
This paper explores how and when to use common random number (CRN) simulation to evaluate Markov chain Monte Carlo (MCMC) convergence rates. We discuss how CRN simulation is closely related to theoretical convergence rate techniques such as one-shot
Externí odkaz:
http://arxiv.org/abs/2309.15735
We review criteria for comparing the efficiency of Markov chain Monte Carlo (MCMC) methods with respect to the asymptotic variance of estimates of expectations of functions of state, and show how such criteria can justify ways of combining improvemen
Externí odkaz:
http://arxiv.org/abs/2305.18268
The Metropolis algorithm involves producing a Markov chain to converge to a specified target density $\pi$. In order to improve its efficiency, we can use the Rejection-Free version of the Metropolis algorithm, which avoids the inefficiency of reject
Externí odkaz:
http://arxiv.org/abs/2210.10513
This paper considers the challenge of designing football group draw mechanisms which have the uniform distribution over all valid draw assignments, but are also entertaining, practical, and transparent. We explain how to simulate the FIFA Sequential
Externí odkaz:
http://arxiv.org/abs/2205.06578
Simulated Annealing using Metropolis steps at decreasing temperatures is widely used to solve complex combinatorial optimization problems. In order to improve its efficiency, we can use the Rejection-Free version of the Metropolis algorithm, which av
Externí odkaz:
http://arxiv.org/abs/2205.02083
This paper gathers together different conditions which are all equivalent to geometric ergodicity of time-homogeneous Markov chains on general state spaces. A total of 34 different conditions are presented (27 for general chains plus 7 just for rever
Externí odkaz:
http://arxiv.org/abs/2203.04395