2期

最新文章

• Multivariate output analysis for Markov chain Monte Carlo AI翻译
  作者：Vats, Dootika; Flegal, James M.; Jones, Galin L.
  作者单位：University of Warwick; University of California System; University of California Riverside; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Markov chain Monte Carlo produces a correlated sample which may be used for estimating expectations with respect to a target distribution. A fundamental question is: when should sampling stop so that we have good estimates of the desired quantities? The key to answering this question lies in assessing the Monte Carlo error through a multivariate Markov chain central limit theorem. The multivariate nature of this Monte Carlo error has been largely ignored in the literature. We present a multiva...
• Kinetic energy choice in Hamiltonian/hybrid Monte Carlo AI翻译
  作者：Livingstone, S.; Faulkner, M. F.; Roberts, G. O.
  作者单位：University of Bristol; University of Warwick
  摘要：We consider how different choices of kinetic energy in Hamiltonian Monte Carlo affect algorithm performance. To this end, we introduce two quantities which can be easily evaluated, the composite gradient and the implicit noise. Results are established on integrator stability and geometric convergence, and we show that choices of kinetic energy that result in heavy-tailed momentum distributions can exhibit an undesirable negligible moves property, which we define. A general efficiency-robustnes...
• Spectral density estimation for random fields via periodic embeddings AI翻译
  作者：Guinness, Joseph
  作者单位：Cornell University
  摘要：We introduce methods for estimating the spectral density of a random field on a d-dimensional lattice from incomplete gridded data. Data are iteratively imputed onto an expanded lattice according to a model with a periodic covariance function. The imputations are convenient computationally, in that circulant embedding and preconditioned conjugate gradient methods can produce imputations in O(n log n) time and O(n) memory. However, these so-called periodic imputations are motivated mainly by th...
• General Bayesian updating and the loss-likelihood bootstrap AI翻译
  作者：Lyddon, S. P.; Holmes, C. C.; Walker, S. G.
  作者单位：University of Oxford; University of Texas System; University of Texas Austin
  摘要：In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same method can be derived, without approximation, under a Bayesian nonparametric model with the parameter of interest defined through minimizing an expected negative loglikelihood under an unknown sampling distribution. This interpretation enables us to extend the weighted likelihood bootstrap to posterior sampling for param...
• Hierarchical Bayes versus empirical Bayes density predictors under general divergence loss AI翻译
  作者：Ghosh, M.; Kubokawa, T.
  作者单位：State University System of Florida; University of Florida; University of Tokyo
  摘要：Consider the problem of finding a predictive density of a new observation drawn independently of observations sampled from a multivariate normal distribution with the same unknown mean vector and the same known variance under general divergence loss. In this paper, we consider two kinds of prior distribution for the mean vector: one is a multivariate normal distribution with mean based on unknown regression coefficients, and the other further assumes that the regression coefficients have unifo...
• Bootstrap of residual processes in regression: to smooth or not to smooth? AI翻译
  作者：Neumeyer, N.; van Keilegom, I.
  作者单位：University of Hamburg; KU Leuven
  摘要：In this paper we consider regression models with centred errors, independent of the covariates. Given independent and identically distributed data and given an estimator of the regression function, which can be parametric or nonparametric in nature, we estimate the distribution of the error term by the empirical distribution of estimated residuals. To approximate the distribution of this estimator, Koul & Lahiri (1994) and Neumeyer (2009) proposed bootstrap procedures based on smoothing the re...
• The debiased Whittle likelihood AI翻译
  作者：Sykulski, Adam M.; Olhede, Sofia C.; Guillaumin, Arthur P.; Lilly, Jonathan M.; Early, Jeffrey J.
  作者单位：Lancaster University; University of London; University College London; NorthWest Research Associates
  摘要：The Whittle likelihood is a widely used and computationally efficient pseudolikelihood. However, it is known to produce biased parameter estimates with finite sample sizes for large classes of models. We propose a method for debiasing Whittle estimates for second-order stationary stochastic processes. The debiased Whittle likelihood can be computed in the same O(n log n) operations as the standard Whittle approach. We demonstrate the superior performance of our method in simulation studies and...
• Unbiased Hamiltonian Monte Carlo with couplings AI翻译
  作者：Heng, J.; Jacob, P. E.
  作者单位：ESSEC Business School; Harvard University
  摘要：We propose a method for parallelization of Hamiltonian Monte Carlo estimators. Our approach involves constructing a pair of Hamiltonian Monte Carlo chains that are coupled in such away that they meet exactly after some random number of iterations. These chains can then be combined so that the resulting estimators are unbiased. This allows us to produce independent replicates in parallel and average them to obtain estimators that are consistent in the limit of the number of replicates, rather t...
• Wasserstein covariance for multiple random densities AI翻译
  作者：Petersen, Alexander; Mueller, Hans-Georg
  作者单位：University of California System; University of California Santa Barbara; University of California System; University of California Davis
  摘要：A common feature of methods for analysing samples of probability density functions is that they respect the geometry inherent to the space of densities. Once a metric is specified for this space, the Frechet mean is typically used to quantify and visualize the average density of the sample. For one-dimensional densities, the Wasserstein metric is popular due to its theoretical appeal and interpretive value as an optimal transport metric, leading to the Wasserstein-Frechet mean or barycentre as...
• Miscellanea Calibrating general posterior credible regions AI翻译
  作者：Syring, Nicholas; Martin, Ryan
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; North Carolina State University
  摘要：Calibration of credible regions derived from under- or misspecified models is an important and challenging problem. In this paper, we introduce a scalar tuning parameter that controls the posterior distribution spread, and develop a Monte Carlo algorithm that sets this parameter so that the corresponding credible region achieves the nominal frequentist coverage probability.