TY - CHAP
T1 - Computational information geometry in statistics
T2 - mixture modelling
AU - Anaya-Izquierdo, Karim
AU - Critchley, Frank
AU - Marriott, Paul
AU - Vos, Paul
PY - 2013
Y1 - 2013
N2 - This paper applies the tools of computation information geometry – 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.
AB - This paper applies the tools of computation information geometry – 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.
UR - http://dx.doi.org/10.1007/978-3-642-40020-9_34
U2 - 10.1007/978-3-642-40020-9_34
DO - 10.1007/978-3-642-40020-9_34
M3 - Book chapter
SN - 9783642400193
VL - IX
T3 - Lecture Notes in Computer Science
SP - 319
EP - 326
BT - Geometric Science of Information
A2 - Nielsen, Frank
A2 - Barbaresco, Frédéric
PB - Springer
CY - Berlin, Germany
ER -