Bayesian estimation of transition probabilities from repeated cross sections

Ben Pelzer; Rob Eisinga

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Bayesian estimation of transition probabilities from repeated cross sections

机译：重复截面的贝叶斯转移概率估计

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摘要

This paper discusses some simple practical advantages of Markov chain Monte Carlo (MCMC) methods in estimating entry and exit transition probabilities from repeated independent surveys. Simulated data are used to illustrate the usefulness of MCMC methods when the likelihood function has multiple local maxima. Actual data on the evaluation of an HIV prevention intervention program among drug users are used to demonstrate the advantage of using prior information to enhance parameter identificaiton. The latter example also demonstrates an important strength of the MCMC approach, namely the ability to make inferences on arbitrary functions of model parameters.

机译：本文讨论了马尔可夫链蒙特卡洛（MCMC）方法在通过反复独立调查估算出入境过渡概率时的一些简单实用优势。仿真数据用于说明当似然函数具有多个局部最大值时MCMC方法的有效性。关于吸毒者中艾滋病毒预防干预计划评估的实际数据用于证明使用先验信息来增强参数识别的优势。后一个示例还证明了MCMC方法的重要优势，即能够对模型参数的任意函数进行推断的能力。

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来源
《Statistica neerlandica》 |2002年第1期|23-33|共11页
作者
Ben Pelzer; Rob Eisinga;
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作者单位

Research Technical Department University of Nijmegen The Netherlandsb.pelzer@maw.kun.nl;

Department of Social Science Research Methods University of Nijmegen The Netherlands;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Bayesian analysis; MCMC; Markov transition models; repeated cross sections;

机译：贝叶斯分析;MCMC;马尔可夫转移模型;重复截面;

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7. Bayesian estimation of transition probabilities from repeated cross sections [O] . Pelzer Ben, Eisinga Rob 2002

机译：重复截面的贝叶斯转移概率估计

Bayesian estimation of transition probabilities from repeated cross sections

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