Illustration of our EM algorithm (for k = 3 at t = 1) that updates a randomly initialized density in (a) through Iterations 1–6 in (b)–(g) to approximate the unknown target density in (h).

Yellow dots in (a)–(g) are the 2-dimensional pilot data , 1000 sampled from the initial distribution . Gray-scale filled contour plots represent the Gaussian mixture density with k = 3 components in (a)–(g) and the target density in (h). White...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	John Y Choe (20890175) (author)
مؤلفون آخرون:	Yen-Chi Chen (3116409) (author), Nick Terry (11326447) (author)
منشور في:	2025
الموضوعات:	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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Illustration of our EM algorithm (for <i>k</i> = 3 at <i>t</i> = 1) that updates a randomly initialized density in (a) through Iterations 1–6 in (b)–(g) to approximate the unknown target density in (h).

مواد مشابهة