Estimating Expected Information Gains for Experimental Designs With Application to the Random Fatigue-Limit Model

Expected gain in Shannon information is commonly suggested as a Bayesian design evaluation criterion. Because estimating expected information gains is computationally expensive, examples in which they have been successfully used in identifying Bayes optimal designs are both few and typically quite simplistic. This article discusses in general some properties of estimators of expected information gains based on Markov chain Monte Carlo (MCMC) and Laplacian approximations. We then investigate some issues that arise when applying these methods to the problem of experimental design in the (technically nontrivial) random fatigue-limit model of Pascual and Meeker. An example comparing follow-up designs for a laminate panel study is provided.     

Sampling Schemes for Bayesian Variable Selection in Generalized Linear Models

Bayesian approaches to prediction and the assessment of predictive uncertainty in generalized linear models are often based on averaging predictions over different models, and this requires methods for accounting for model uncertainty. When there are linear dependencies among potential predictor variables in a generalized linear model, existing Markov chain Monte Carlo algorithms for sampling from the posterior distribution on the model and parameter space in Bayesian variable selection problems may not work well. This article describes a sampling algorithm based on the Swendsen-Wang algorithm for the Ising model, and which works well when the predictors are far from orthogonality. In problems of variable selection for generalized linear models we can index different models by a binary parameter vector, where each binary variable indicates whether or not a given predictor variable is included in the model. The posterior distribution on the model is a distribution on this collection of binary strings, and by thinking of this posterior distribution as a binary spatial field we apply a sampling scheme inspired by the Swendsen-Wang algorithm for the Ising model in order to sample from the model posterior distribution. The algorithm we describe extends a similar algorithm for variable selection problems in linear models. The benefits of the algorithm are demonstrated for both real and simulated data.     

Antithetic Methods for Gibbs Samplers

In this article we propose a modification to the output from Metropolis-within-Gibbs samplers that can lead to substantial reductions in the variance over standard estimates. The idea is simple: at each time step of the algorithm, introduce an extra sample into the estimate that is negatively correlated with the current sample, the rationale being that this provides a two-sample numerical approximation to a Rao–Blackwellized estimate. As the conditional sampling distribution at each step has already been constructed, the generation of the antithetic sample often requires negligible computational effort. Our method is implementable whenever one subvector of the state can be sampled from its full conditional and the corresponding distribution function may be inverted, or the full conditional has a symmetric density. We demonstrate our approach in the context of logistic regression and hierarchical Poisson models. The data and computer code used in this article are available online.     

Continuous Contour Monte Carlo for Marginal Density Estimation With an Application to a Spatial Statistical Model

The problem of marginal density estimation for a multivariate density function f(x) can be generally stated as a problem of density function estimation for a random vector λ(x) of dimension lower than that of x. In this article, we propose a technique, the so-called continuous Contour Monte Carlo (CCMC) algorithm, for solving this problem. CCMC can be viewed as a continuous version of the contour Monte Carlo (CMC) algorithm recently proposed in the literature. CCMC abandons the use of sample space partitioning and incorporates the techniques of kernel density estimation into its simulations. CCMC is more general than other marginal density estimation algorithms. First, it works for any density functions, even for those having a rugged or unbalanced energy landscape. Second, it works for any transformation λ(x) regardless of the availability of the analytical form of the inverse transformation. In this article, CCMC is applied to estimate the unknown normalizing constant function for a spatial autologistic model, and the estimate is then used in a Bayesian analysis for the spatial autologistic model in place of the true normalizing constant function. Numerical results on the U.S. cancer mortality data indicate that the Bayesian method can produce much more accurate estimates than the MPLE and MCMLE methods for the parameters of the spatial autologistic model.     

Towards a Faster Implementation of Density Estimation With Logistic Gaussian Process Priors

A novel method is proposed to compute the Bayes estimate for a logistic Gaussian process prior for density estimation. The method gains speed by drawing samples from the posterior of a finite-dimensional surrogate prior, which is obtained by imputation of the underlying Gaussian process. We establish that imputation results in quite accurate computation. Simulation studies show that accuracy and high speed can be combined. This fact, along with known flexibility of the logistic Gaussian priors for modeling smoothness and recent results on their large support, makes these priors and the resulting density estimate very attractive.     

散度偏大计数数据回归模型的变量选择与模型比较

讨论了具有散度偏大特征计数数据的建模与拟合问题.针对导致数据散度偏大的原因和常用的几类候选模型的结构,分别给出了关于嵌套模型的模型与变量同时选择的Bayes方法和关于非嵌套模型的模型检验与比较方法,并在此基础上进一步完善,提出了较为系统完整的模型与变量选择方法.实际例子说明了方法的具体实现过程和有效性.     

Some generalized limit theorems concerning delayed sums of random sequence

For a sequence of arbitrarily dependent m-valued random variables (Xn) n∈N , the generalized strong limit theorem of the delayed average is investigated. In our proof, we improved the method proposed by Liu [6] . As an application, we also studied some limit properties of delayed average for inhomogeneous Markov chains.     

基于网络均衡的电力市场排污权交易政策研究

本文构建了电力供应链网络均衡模型,网络中包含三级决策主体,分别是:发电厂、电力服务商和用电市场.在分析各级决策者行为的基础上,得到了基于变分不等式的均衡条件,研究了针对发电厂的排污权交易政策设计问题.研究发现:排污交易政策实施后,总排污量不会超过给定的排污权总量;发电厂清洁生产能力的差异对排污权交易政策影响显著.     

公平偏好下基于批发价契约的供应链 协调研究

文中基于Nash讨价还价博弈思想建立公平偏好框架,构建公平偏好效用体系,以此为基础对采用批发价契约的报童模型展开行为研究,采用数理模型和数值分析方法分析了零售商和供应商的公平偏好行为对零售商和供应链系统最优订货量的影响,即零售商和供应商同时关注公平时,零售商和供应链系统的最优订货量趋于保守;并发现零售商和供应链系统的最优订货量随零售商的公平偏好程度增加而递减,但随着供应商公平偏好程度增加而递增,且供应链系统最优订货量变化趋势比零售商明显．然后,在此基础上分析比较得到,无论供应商和零售商是否偏好公平,批发价契约都不能实现供应链协调．最后,对批发价、零售价、供应商生产成本、零售商缺货成本和供应商缺货成本进行敏感度分析．     

Brownian localization： A generalized coupling model yielding a nonergodic Langevin equation description

A minimal system-plus-reservoir model yielding a nonergodic Langevin equation is proposed, which originates from the cubic-spectral density of environmental oscillators and momentum-dependent coupling. This model allows ballistic diffusion and classical localization simultaneously, in which the fluctuation-dissipation relation is still satisfied but the Khinchin theorem is broken. The asymptotical equilibrium for a nonergodic system requires the initial thermal equilibrium, however, when the system starts from nonthermal conditions, it does not approach the equilibration even though a nonlinear potential is used to bound the particle, this can be confirmed by the zerotb law of thermodynamics. In the dynamics of Brownian localization, due to the memory damping function inducing a constant term, our results show that the stationary distribution of the system depends on its initial preparation of coordinate rather than momentum. The coupled oscillator chain with a fixed end boundary acts as a heat bath, which has long been used in studies of collinear atom/solid-surface scattering and lattice vibration, we investigate this problem from the viewpoint of nonergodicity.