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通过模型定向掩蔽与多重插补平衡推断完整性和披露风险AI 解读

There is a growing expectation that data collected by government-funded studies should be openly available to ensure research reproducibility, and so is the concern on data-privacy. A strategy to protect individuals' identity is to release multiply imputed (MI) synthetic datasets with masked sensitivity values (Rubin, 1993). However, information loss or incorrectly specified imputation models can weaken or invalidate the inferences obtained from the MI-datasets. Studying a…

利用零散数据重建历史人口及其不确定性AI 解读

Current methods for reconstructing human populations of the past by age and sex are deterministic or do not formally account for measurement error. We propose a method for simultaneously estimating age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data that incorporates measurement error. Inference is based on joint posterior probability distributions that yield fully probabilistic interval estimates. It…

用于局地校准概率定量降水预测的地统计模型平均AI 解读

Accurate weather benefit many key societal functions and activities, including agriculture, transportation, recreation, and basic human and infrastructural safety. Over the past two decades, ensembles of numerical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate probabilistic forecasts for future weather events. However, ensemble systems are uncalibrated and biased, and thus need to be…

贝叶斯模型平均中的默认先验与预测表现：增长决定因素应用AI 解读

Bayesian model averaging (BMA) has become widely accepted as a way of accounting for model uncertainty, notably in regression models for identifying the determinants of economic growth. To implement BMA the user must specify a prior distribution in two parts: a prior for the regression parameters and a prior over the model space. Here we address the issue of which default prior to use for BMA in linear regression. We compare 12 candidate parameter priors: the unit…

服务于冬季道路养护的概率天气预测AI 解读

Winter road maintenance is one of the main tasks for the Washington State Department of Transportation. Anti-icing, that is, the preemptive application of chemicals, is often used to keep the roadways free of ice. Given the preventive nature of anti-icing, accurate predictions of road ice are needed. Currently, anti-icing decisions are usually based on deterministic weather forecasts. However, the costs of the two kinds of errors are highly asymmetric because the cost of a…

基于集合预报与贝叶斯模型平均的风速概率预测AI 解读

The current weather forecasting paradigm is deterministic, based on numerical models. Multiple estimates of the current state of the atmosphere are used to generate an ensemble of deterministic predictions. Ensemble forecasts, while providing information on forecast uncertainty, are often uncalibrated. Bayesian model averaging (BMA) is a statistical ensemble postprocessing method that creates calibrated predictive probability density functions (PDFs). Probabilistic wind…

概率预测、校准与集中度AI 解读

Summary Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the…

确定性模拟模型的推断：贝叶斯融合方法AI 解读

Deterministic simulation models are used in many areas of science, engineering, and policy making. Typically, these are complex models that attempt to capture underlying mechanisms in considerable detail, and they have many user-specified inputs. The inputs are often specified by some form of trial-and-error approach in which plausible values are postulated, the corresponding outputs inspected, and the inputs modified until plausible outputs are obtained. Here we address the…

贝叶斯形态学：快速无监督贝叶斯图像分析AI 解读

We consider the problems of image segmentation and classification, and image restoration when the true image is made up of a small number of (unordered) colors. Our emphasis is on both performance and speed; speed has become increasingly important for analyzing large images and multispectral images with many bands, processing large image databases, real-time or near realtime image analysis, and the online analysis of video. Bayesian image analysis provides an elegant…

贝叶斯形态学：快速无监督贝叶斯图像分析AI 解读

We consider the problems of image segmentation and classification, and image restoration when the true image is made up of a small number of (unordered) colors. Our emphasis is on both performance and speed; speed has become increasingly important for analyzing large images and multispectral images with many bands, processing large image databases, real-time or near realtime image analysis, and the online analysis of video. Bayesian image analysis provides an elegant…

利用最近邻去除杂点以估计空间点过程特征AI 解读

We consider the problem of detecting features in spatial point processes in the presence of substantial clutter. One example is the detection of minefields using reconnaissance aircraft images that identify many objects that are not mines. Our solution uses Kth nearest neighbor distances of points in the process to classify them as clutter or otherwise. The observed Kth nearest neighbor distances are modeled as a mixture distribution, the parameters of which are estimated by…

通过模型聚类识别含杂波的空间点过程特征AI 解读

We consider the problem of detecting features, such as minefields or seismic faults, in spatial point processes when there is substantial clutter. We use model-based clustering based on a mixture model for the process, in which features are assumed to generate points according to highly linear multivariate normal densities, and the clutter arises according to a spatial Poisson process. Nonlinear features are represented by several densities, giving a piecewise linear…

利用后验模拟与Laplace–Metropolis估计量估计贝叶斯因子AI 解读

The key quantity needed for Bayesian hypothesis testing and model selection is the integrated, or marginal, likelihood of a model. We describe a way to use posterior simulation output to estimate integrated likelihoods. We describe the basic Laplace—Metropolis estimator for models without random effects. For models with random effects, we introduce the compound Laplace-Metropolis estimator. We apply this estimator to data from the World Fertility Survey and show it to give…

摘要暂缺

线性回归模型的贝叶斯模型平均AI 解读

We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem involves averaging over all possible models (i.e., combinations of predictors) when making inferences about quantities of interest. This approach is often not practical. In this…

用混合转移分布模型描述时间序列的平坦段、突发与离群值AI 解读

The class of mixture transition distribution (MTD) time series models is extended to general non-Gaussian time series. In these models the conditional distribution of the current observation given the past is a mixture of conditional distributions given each one of the last p observations. They can capture non-Gaussian and nonlinear features such as flat stretches, bursts of activity, outliers changepoints in a single unified model class. They can also represent time series…

摘要暂缺

适用于强非线性关系多元密度的局部自适应重要性抽样AI 解读

We consider adaptive importance sampling techniques that use kernel density estimates at each iteration as importance sampling functions. These can provide more nearly constant importance weights and more precise estimates of quantities of interest than the sampling importance resampling algorithm when the initial importance sampling function is diffuse relative to the target. We propose a new method that adapts to the varying local structure of the target. When the target…

存在加性异常值的自回归过程的稳健贝叶斯模型选择AI 解读

Autoregressive (AR) models of order k are often used for forecasting and control of time series, as well as for the estimation of functionals such as the spectrum. Here we propose a method that consists of calculating the posterior probabilities of the competing AR(k) models in a way that is robust to outliers, and then obtaining the predictive distributions of quantities of interest, such as future observations and the spectrum, as a weighted average of the predictive…

Medicare 脑卒中患者转入专业护理机构的比例：含未观测异质性的贝叶斯逻辑回归AI 解读

We determine factors, both hospital-specific and market area-specific, associated with hospitals' propensities for discharging Medicare stroke patients to skilled nursing facilities (SNF's) in California and Florida. Logistic regression is generalized to the case of a betabinomial, hierarchical model, in which covariate information is included in the hyperparameters of the second-stage beta distribution. It is found that the posterior mean of the proportion discharged to SNF…

含加性异常值的自回归过程稳健贝叶斯模型选择AI 解读

Autoregressive (AR) models of order k are often used for forecasting and control of time series, as well as for the estimation of functionals such as the spectrum. Here we propose a method that consists of calculating the posterior probabilities of the competing AR(k) models in a way that is robust to outliers, and then obtaining the predictive distributions of quantities of interest, such as future observations and the spectrum, as a weighted average of the predictive…

Medicare 中风患者转入专业护理机构的比例：含未观测异质性的贝叶斯逻辑回归AI 解读

We determine factors, both hospital-specific and market area-specific, associated with hospitals' propensities for discharging Medicare stroke patients to skilled nursing facilities (SNF's) in California and Florida. Logistic regression is generalized to the case of a betabinomial, hierarchical model, in which covariate information is included in the hyperparameters of the second-stage beta distribution. It is found that the posterior mean of the proportion discharged to SNF…

贝叶斯因子AI 解读

In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is one-half. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P-values, less attention has been given to the Bayes factor as a…

弓头鲸确定性种群动力学模型的统计推断AI 解读

We consider the problem of inference about a quantity of interest given different sources of information linked by a deterministic population dynamics model. Our approach consists of translating all the available information into a joint premodel distribution on all the model inputs and outputs and then restricting this to the submanifold defined by the model to obtain the joint postmodel distribution. Marginalizing this yields inference, conditional on the model, about…