Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Johndrow, James"'
Modern mobile applications such as navigation services and ride-sharing platforms rely heavily on geospatial technologies, most critically predictions of the time required for a vehicle to traverse a particular route, or the so-called estimated time
Externí odkaz:
http://arxiv.org/abs/2112.09993
Autor:
Melikechi, Omar, Young, Alexander L., Tang, Tao, Bowman, Trevor, Dunson, David, Johndrow, James
The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model paramete
Externí odkaz:
http://arxiv.org/abs/2112.07039
Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment
Risk assessment instruments are used across the criminal justice system to estimate the probability of some future behavior given covariates. The estimated probabilities are then used in making decisions at the individual level. In the past, there ha
Externí odkaz:
http://arxiv.org/abs/2102.01135
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
Understanding the number of individuals who have been infected with the novel coronavirus SARS-CoV-2, and the extent to which social distancing policies have been effective at limiting its spread, are critical for effective policy going forward. Here
Externí odkaz:
http://arxiv.org/abs/2004.02605
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.
