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Titlebook: Conditional Specification of Statistical Models; Barry C. Arnold,Enrique Castillo,José María Sarabi Book 1999 Springer Science+Business Me

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发表于 2025-3-21 17:41:37 | 显示全部楼层 |阅读模式
书目名称Conditional Specification of Statistical Models
编辑Barry C. Arnold,Enrique Castillo,José María Sarabi
视频videohttp://file.papertrans.cn/236/235211/235211.mp4
丛书名称Springer Series in Statistics
图书封面Titlebook: Conditional Specification of Statistical Models;  Barry C. Arnold,Enrique Castillo,José María Sarabi Book 1999 Springer Science+Business Me
出版日期Book 1999
关键词Statistica; Statistical Models; addition; boundary element method; development; distribution; equation; fun
版次1
doihttps://doi.org/10.1007/b97592
isbn_softcover978-1-4757-7260-9
isbn_ebook978-0-387-22588-3Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer Science+Business Media New York 1999
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Transactions on Computational Science IX The key reference for the present chapter is Arnold and Strauss (1991). However it should be mentioned that results due to Besag (1974), in a stochastic process setting, anticipate some of the observations in this chapter.
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Conditionals in Exponential Families, The key reference for the present chapter is Arnold and Strauss (1991). However it should be mentioned that results due to Besag (1974), in a stochastic process setting, anticipate some of the observations in this chapter.
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Marginal and Conditional Specification in General,rveying marginal and conditional specification in the bivariate case and then enumerate carefully the available results in higher dimensions. Throughout we will assume absolute continuity with respect to some convenient dominating measure.
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Magdy A. El-Tawil,Noha A. Al-Mullamatically natural multivariate extensions of univariate survival models. In this sense they deserve the name “multivariate survival distributions,” though the same should not be construed as implying limitations in their potential fields of applications.
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Polymorphic Particle Swarm Optimizationtc. Much early Bayesian work was focussed on so-called conjugate priors. Such priors are convenient but lack flexibility for modeling informed prior belief. The classical scenario, in which most of the issues are already clearly visible, involves a sample from a normal distribution with unknown mean and variance.
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