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Posterior Simulation in Countable Mixture Models for Large Datasets

大数据集可数混合模型的后验模拟

Mixture models, or convex combinations of a countable number of probability distributions, offer an elegant framework for inference when the population of interest can be subdivided into latent clusters having random characteristics that are heterogeneous between, but homogeneous within, the clusters. Traditionally, the different kinds of mixture models have been motivated and analyzed from very different perspectives, and their common characteristics have not been fully…

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Bayesian Hidden Markov Modeling of Array CGH Data

阵列比较基因组杂交数据的贝叶斯隐马尔可夫建模

Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic…

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Mixture Cure Survival Models With Dependent Censoring

允许依赖删失的混合治愈生存模型

Summary The paper is motivated by cure detection among the prostate cancer patients in the National Institutes of Health surveillance epidemiology and end results programme, wherein the main end point (e.g. deaths from prostate cancer) and the censoring causes (e.g. deaths from heart diseases) may be dependent. Although many researchers have studied the mixture survival model to analyse survival data with non-negligible cure fractions, none has studied the mixture cure model…

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Generalized Poststratification and Importance Sampling for Subsampled Markov Chain Monte Carlo Estimation

子抽样 MCMC 估计的广义后分层与重要性抽样

Benchmark estimation is motivated by the goal of producing an approximation to a posterior distribution that is better than the empirical distribution function. This is accomplished by incorporating additional information into the construction of the approximation. We focus here on generalized poststratification, the most successful implementation of benchmark estimation in our experience. We develop generalized poststratification for settings where the source of the…

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