4期

最新文章

• Fast increased fidelity samplers for approximate Bayesian Gaussian process regression AI翻译
  作者：Moran, Kelly R.; Wheeler, Matthew W.
  作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
  摘要：Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H-matrix approximations that scales at O(nlog2n). We show that this approximation's Kullback-Leibler divergence to the true posterior can be ma...
• Bayesian context trees: Modelling and exact inference for discrete time series AI翻译
  作者：Kontoyiannis, Ioannis; Mertzanis, Lambros; Panotopoulou, Athina; Papageorgiou, Ioannis; Skoularidou, Maria
  作者单位：University of Cambridge; Dartmouth College; University of Cambridge; University of Cambridge; MRC Biostatistics Unit
  摘要：We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg-orithm can compute the prior predictive likelihood exa-ctly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact p...
• Bayesian inference for risk minimization via exponentially tilted empirical likelihood AI翻译
  作者：Tang, Rong; Yang, Yun
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The celebrated Bernstein von-Mises theorem ensures credible regions from a Bayesian posterior to be well-calibrated when the model is correctly-specified, in the frequentist sense that their coverage probabilities tend to the nominal values as data accrue. However, this conventional Bayesian framework is known to lack robustness when the model is misspecified or partly specified, for example, in quantile regression, risk minimization based supervised/unsupervised learning and robust estimation...
• Paired or partially paired two-sample tests with unordered samples AI翻译
  作者：Wang, Yudong; Tang, Yanlin; Ye, Zhi-Sheng
  作者单位：National University of Singapore; East China Normal University
  摘要：In paired two-sample tests for mean equality, it is common to encounter unordered samples in which subject identities are not observed or unobservable, and it is impossible to link the measurements before and after treatment. The absence of subject identities masks the correspondence between the two samples, rendering existing methods inapplicable. In this paper, we propose two novel testing approaches. The first splits one of the two unordered samples into blocks and approximates the populati...
• Simulation of multivariate diffusion bridges (vol 78, pg 343, 2016) AI翻译
  作者：Bladt, Mogens; Finch, Samuel; Sorensen, Michael
  作者单位：University of Copenhagen
  摘要：We correct an error in Theorem 1 in Bladt et al. (2016) Journal of the Royal Statistical Society: Series B, 78, 343-369 by changing the initial distribution of an auxiliary diffusion process, which is used to describe the distribution of the proposed approximate diffusion bridges. As a consequence, we correct two algorithms for simulating exact diffusion bridges by changing the initial distribution of auxiliary diffusion processes in the same way. Simulation studies affected by the error are r...
• Nonparametric, tuning-free estimation of S-shaped functions AI翻译
  作者：Feng, Oliver Y.; Chen, Yining; Han, Qiyang; Carroll, Raymond J.; Samworth, Richard J.
  作者单位：University of Cambridge; University of London; London School Economics & Political Science; Rutgers University System; Rutgers University New Brunswick; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：We consider the nonparametric estimation of an S-shaped regression function. The least squares estimator provides a very natural, tuning-free approach, but results in a non-convex optimization problem, since the inflection point is unknown. We show that the estimator may nevertheless be regarded as a projection onto a finite union of convex cones, which allows us to propose a mixed primal-dual bases algorithm for its efficient, sequential computation. After developing a projection framework th...
• Semiparametric latent class analysis of recurrent event data AI翻译
  作者：Zhao, Wei; Peng, Limin; Hanfelt, John
  作者单位：Emory University; Shandong University
  摘要：Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. I...
• Universal prediction band via semi-definite programming AI翻译
  作者：Liang, Tengyuan
  作者单位：University of Chicago
  摘要：We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverag...
• Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models AI翻译
  作者：Graham, Matthew M.; Thiery, Alexandre H.; Beskos, Alexandros
  作者单位：University of London; University College London; National University of Singapore
  摘要：Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology-borrowing ideas from statistical physics and computational chemistry-for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process. Joint configurations of the underlying...
• Optimal thinning of MCMC output AI翻译
  作者：Riabiz, Marina; Chen, Wilson Ye; Cockayne, Jon; Swietach, Pawel; Niederer, Steven A.; Mackey, Lester; Oates, Chris J.
  作者单位：University of London; King's College London; Alan Turing Institute; University of Sydney; University of Oxford; Microsoft; Newcastle University - UK
  摘要：The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Typically a number of the initial states are attributed to 'burn in' and removed, while the remainder of the chain is 'thinned' if compression is also required. In this paper, we consider the problem of retrospectively selecting a subset of states, of fixed cardinality, from the sample path such that the approximation...