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.
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Anaya-Izquierdo, K., Critchley, F., Marriott, P., Vos, P. (2013). Computational Information Geometry in Statistics: Mixture Modelling. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_34
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DOI: https://doi.org/10.1007/978-3-642-40020-9_34
Publisher Name: Springer, Berlin, Heidelberg
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