Nonparametric Bayes inference for concave distribution functions

Martin B. Hansen; Steffen L. Lauritzen

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Nonparametric Bayes inference for concave distribution functions

机译：凹分布函数的非参数贝叶斯推断

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摘要

Bayesian inference for concave distribution functions is investigated. This is made by transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. We give a method for sampling from the posterior distribution using a Pólya urn scheme in combination with a Markov chain Monte Carlo algorithm. The methods are extended to estimation of concave distribution functions for incompletely observed data.

机译：研究了凹分布函数的贝叶斯推断。这是通过将分布函数空间上的Dirichlet过程的混合转换为凹分布函数空间来实现的。我们给出了一种使用Pólya缸方案与马尔可夫链蒙特卡洛算法相结合的后验分布进行采样的方法。该方法扩展到估计不完整观测数据的凹面分布函数。

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《Statistica neerlandica》 |2002年第1期|110-127|共18页
作者
Martin B. Hansen; Steffen L. Lauritzen;
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作者单位

Department of Mathematical Sciences AalborgØ University Denmarkmbh@math.auc.dk;

Department of Mathematical Sciences AalborgØ University Denmarkmbh@math.auc.dk;

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正文语种 eng
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关键词
Dirichlet process; Markov chain Monte Carlo; concave distribution functions; decreasing density function; order restricted inference; multiplicative censoring; Pólya urn scheme.;

机译：Dirichlet过程;Markov链蒙特卡洛;凹分布函数;递减密度函数;有序限制推理;乘法删失;Pólyaurn方案。;

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Nonparametric Bayes inference for concave distribution functions

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