Importance-Weighted Marginal Bayesian Posterior Density Estimation
作者:
Ming-Hui Chen,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 427
页码: 818-824
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476815
出版商: Taylor & Francis Group
关键词: Conditional density estimation;Kernel density estimation;Markov chain sampling;Monte Carlo;Simulation
数据来源: Taylor
摘要:
Markov chain sampling schemes generate dependent observations {Θi, 0 ≤ i ≤ n} from a full joint posterior distribution π(θdata). Frequently, only certain marginals of this full posterior density are of interest; thus an interesting problem is how to estimate the marginal posterior densities based on the dependent observations {Θi, 0 ≤ i ≤ n} from π(θ data). We propose a new importance-weighted marginal density estimation (IWMDE) method. An IWMDE is obtained by averaging many dependent observations of the ratio of the full joint posterior densities multiplied by a weighting conditional densityw.The asymptotic properties for the IWMDE and the guidelines for choosing a weighting conditional densityware also considered. A bivariate normal model and a constrained linear multiple regression model are used to illustrate how to derive the IWMDE's for the marginal posterior densities.
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