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pro vyhledávání: '"Johndrow, James E."'
We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when eac
Externí odkaz:
http://arxiv.org/abs/2012.04798
It is widely known that the performance of Markov chain Monte Carlo (MCMC) can degrade quickly when targeting computationally expensive posterior distributions, such as when the sample size is large. This has motivated the search for MCMC variants th
Externí odkaz:
http://arxiv.org/abs/2010.12514
In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of so
Externí odkaz:
http://arxiv.org/abs/1810.08255
Autor:
Hosseini, Bamdad, Johndrow, James E
We study a class of Metropolis-Hastings algorithms for target measures that are absolutely continuous with respect to a large class of non-Gaussian prior measures on Banach spaces. The algorithm is shown to have a spectral gap in a Wasserstein-like s
Externí odkaz:
http://arxiv.org/abs/1810.00297
Autor:
Johndrow, James E., Smith, Aaron
A well-known folklore result in the MCMC community is that the Metropolis-Hastings algorithm mixes quickly for any unimodal target, as long as the tails are not too heavy. Although we've heard this fact stated many times in conversation, we are not a
Externí odkaz:
http://arxiv.org/abs/1806.07047
Autor:
Buch, David A.1 (AUTHOR) david.buch@duke.edu, Johndrow, James E.2 (AUTHOR), Dunson, David B.1 (AUTHOR)
Publikováno v:
Biometrics. Dec2023, Vol. 79 Issue 4, p2987-2997. 11p.
Autor:
Johndrow, James E., Palacios, Julia A.
Recovery of population size history from molecular sequence data is an important problem in population genetics. Inference commonly relies on a coalescent model linking the population size history to genealogies. The high computational cost of estima
Externí odkaz:
http://arxiv.org/abs/1711.05724
We give a number of results on approximations of Markov kernels in total variation and Wasserstein norms weighted by a Lyapunov function. The results are applied to examples from Bayesian statistics where approximations to transition kernels are made
Externí odkaz:
http://arxiv.org/abs/1711.05382
Autor:
Goldberg, David, Johndrow, James E.
A/B testing is ubiquitous within the machine learning and data science operations of internet companies. Generically, the idea is to perform a statistical test of the hypothesis that a new feature is better than the existing platform---for example, i
Externí odkaz:
http://arxiv.org/abs/1710.03410
This simple note lays out a few observations which are well known in many ways but may not have been said in quite this way before. The basic idea is that when comparing two different Markov chains it is useful to couple them is such a way that they
Externí odkaz:
http://arxiv.org/abs/1706.02040