✦ LIBER ✦

Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm

✍ Scribed by Peter Arcidiacono; John Bailey Jones

Book ID: 108556219
Publisher: John Wiley and Sons
Year: 2003
Tongue: English
Weight: 125 KB
Volume: 71
Category: Article
ISSN: 0012-9682
DOI: 10.1111/1468-0262.00431

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A hybrid EM/Gauss-Newton algorithm for m

A hybrid EM/Gauss-Newton algorithm for maximum likelihood in mixture distributions

✍ Murray Aitkin; Irit Aitkin 📂 Article 📅 1996 🏛 Springer US 🌐 English ⚖ 344 KB

A faster alternative to the EM algorithm in finite mixture distributions is described, which alternates EM iterations with Gauss Newton iterations using the observed information matrix. At the expense of modest additional analytical effort in obtaining the observed information, the hybrid algorithm

Finite Mixture Modeling with Mixture Out

Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm

✍ Bengt Muthén; Kerby Shedden 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 630 KB

Another interpretation of the EM algorit

Another interpretation of the EM algorithm for mixture distributions

✍ Richard J. Hathaway 📂 Article 📅 1986 🏛 Elsevier Science 🌐 English ⚖ 241 KB

A distributed EM algorithm to estimate t

A distributed EM algorithm to estimate the parameters of a finite mixture of components

✍ Behrooz Safarinejadian; Mohammad B. Menhaj; Mehdi Karrari 📂 Article 📅 2009 🏛 Springer-Verlag 🌐 English ⚖ 701 KB

Maximum likelihood inference for mixture

Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

✍ Hsiu J. Ho; Saumyadipta Pyne; Tsung I. Lin 📂 Article 📅 2011 🏛 Springer US 🌐 English ⚖ 715 KB

Learning mixtures of point distribution

Learning mixtures of point distribution models with the EM algorithm

✍ Abdullah A. Al-Shaher; Edwin R. Hancock 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 409 KB

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the