Gaussian mixture models (GMMs) with k components in (a)–(e) correspond to the circles in Fig 2 and are compared with the target density in (f), which is the optimal importance sampling density in the structural safety example (with b = 1 . 5) in Appendix C.

Gaussian mixture models (GMMs) with k components in (a)–(e) correspond to the circles in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0317430#pone.0317430.g002" target="_blank">Fig 2</a> and are compared with the ta...

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Bibliographic Details
Main Author:	John Y Choe (20890175) (author)
Other Authors:	Yen-Chi Chen (3116409) (author), Nick Terry (11326447) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified specified distribution families multiple numerical studies gaussian mixture components g ., select asymptotically unbiased estimator proposed method selects akaike information criterion bayesian information criterion parametric approximation approach entropy information criterion model selection problem density approximation problem information criterion parametric model iterative method bayesian inference problem cannot unknown density target density posterior density optimal density xlink "> well approximates traditional approaches regularity conditions parameters ). paper considers normalizing constant multiplicative constant instead aim importance sampling finite number bic ). best number best model
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Gaussian mixture models (GMMs) with <i>k</i> components in (a)–(e) correspond to the circles in Fig 2 and are compared with the target density in (f), which is the optimal importance sampling density in the structural safety example (with <i>b</i> = 1 . 5) in Appendix C.

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