On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply
On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply
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发表时间：
1997
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通讯作者：
S. Richardson;P. Green;Christian P. Robert;M. Aitkin;David R. Cox;Matthew Stephens;A. Polymenis;W. Gilks;A. Nobile;M. Hodgson;Anthony O'Hagan;N. Longford;A. Dawid;Anthony C. Atkinson;J. Bernardo;J. Besag;Stephen Brooks;S. Byers;A. Raftery;G. Celeux;R. Cheng;W. B. Liu;Yung-Hsin Chien;Edward I. George;N. Cressie;H.-C. Huang;M. Gruet;S. C. Heath;C. Jennison;Andrew B. Lawson;Allan Clark;Geoffrey J. McLachlan;D. Peel;K. Mengersen;A. George;Anne Philippe;Kathryn Roeder;Larry Wasserman;Peter Schlattmann;D. Böhning;D. M. Titterington;H. Tong;M. West
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作者：
S. Richardson;P. Green;Christian P. Robert;M. Aitkin;David R. Cox;Matthew Stephens;A. Polymenis;W. Gilks;A. Nobile;M. Hodgson;Anthony O'Hagan;N. Longford;A. Dawid;Anthony C. Atkinson;J. Bernardo;J. Besag;Stephen Brooks;S. Byers;A. Raftery;G. Celeux;R. Cheng;W. B. Liu;Yung-Hsin Chien;Edward I. George;N. Cressie;H.-C. Huang;M. Gruet;S. C. Heath;C. Jennison;Andrew B. Lawson;Allan Clark;Geoffrey J. McLachlan;D. Peel;K. Mengersen;A. George;Anne Philippe;Kathryn Roeder;Larry Wasserman;Peter Schlattmann;D. Böhning;D. M. Titterington;H. Tong;M. West
完全贝叶斯混合物分析的新方法的开发，利用可逆跳马尔可夫链蒙特卡罗方法，是能够跳之间的参数子空间对应的混合物中的不同数量的组件。由此生成来自所有未知变量的完全联合分布的样本，并且这可以用作后验分布的许多方面的全面呈现的基础。该方法在这里应用于单变量正态混合物的分析，使用分层先验模型，提供了一种方法来处理弱先验信息，同时避免在混合物的上下文中使用不正确的先验的数学陷阱。
New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.