Zobrazeno 1 - 10
of 312
pro vyhledávání: '"Fox, Colin"'
In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site
Externí odkaz:
http://arxiv.org/abs/2405.00397
Autor:
Lethcoe, Kyle, Fox, Colin A., Hafiane, Anouar, Kiss, Robert S., Liu, Jianfang, Ren, Gang, Ryan, Robert O.
Publikováno v:
In BBA - Biomembranes October 2024 1866(7)
We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target distribution. Broadly, the method rewrites the Multilevel MCMC approach
Externí odkaz:
http://arxiv.org/abs/2202.03876
The standard models of sequence evolution on a tree determine probabilities for every character or site pattern. A flattening is an arrangement of these probabilities into a matrix, with rows corresponding to all possible site patterns for one set $A
Externí odkaz:
http://arxiv.org/abs/2111.14961
Publikováno v:
COMPEL -The international journal for computation and mathematics in electrical and electronic engineering, 2023, Vol. 42, Issue 5, pp. 1103-1114.
We address the inverse Frobenius--Perron problem: given a prescribed target distribution $\rho$, find a deterministic map $M$ such that iterations of $M$ tend to $\rho$ in distribution. We show that all solutions may be written in terms of a factoriz
Externí odkaz:
http://arxiv.org/abs/2106.00177
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equati
Externí odkaz:
http://arxiv.org/abs/2012.05668
Autor:
Lee, Bryan, Kharal, Gita, Sreenan, Benjamin, Lin, Claire, Zeng, Ruosheng, Fox, Colin A., Ellison, Patricia, Ryan, Robert O., Brett, Paul J., AuCoin, David, Zhu, Xiaoshan
Publikováno v:
In Journal of Pharmaceutical and Biomedical Analysis 20 January 2024 238
Bayesian modelling and computational inference by Markov chain Monte Carlo (MCMC) is a principled framework for large-scale uncertainty quantification, though is limited in practice by computational cost when implemented in the simplest form that req
Externí odkaz:
http://arxiv.org/abs/2009.08782