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Computational information geometry in statistics: Mixture modelling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2013) 8085 LNCS 319-326
DOI: 10.1007/978-3-642-40020-9_34
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Abstract

This paper applies the tools of computation information geometry [3] - in particular, high dimensional extended multinomial families as proxies for the 'space of all distributions' - in the inferentially demanding area of statistical mixture modelling. A range of resultant benefits are noted. © 2013 Springer-Verlag.

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Anaya-Izquierdo, K., Critchley, F., Marriott, P., & Vos, P. (2013). Computational information geometry in statistics: Mixture modelling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8085 LNCS, pp. 319–326). https://doi.org/10.1007/978-3-642-40020-9_34

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