TY - CHAP
TI - Telescoping mixtures - Learning the number of components and data clusters in Bayesian mixture analysis
AB - Telescoping mixtures are an extension of sparse finite mixtures (Malsiner-Walli, Fruhwirth-Schnatter and Grun, 2016) by assuming that additional to the unknown number of data clusters also the number of mixture components is unknown and has to be estimated. Telescoping mixtures explicitly distinguish between the number of data clusters K+ and components K in the mixture distribution, and purposely allow for more components thandata clusters. By linking the prior on the number of components to the prior on the mixture weights, it is guaranteed that components remain empty as K increases, making the number of clusters in the data, defined through the partition implied by the allocation variables, random apriori. Telescoping mixtures can be seen as an alternative to infinite mixtures models. We present a simple algorithm for posterior MCMC sampling to jointly sample K, the number of components, and K+, the number of data clusters. The algorithm is compared to standard transdimensional algorithm such as the reversible jump Markov chain Monte Carlo (Richardson and Green, 1997) and the Jain-Neal split-merge sampler (Miller and Harrison, 2018).
AF - Statistics5@Aegina
PP - Aegina
UR - https://aueb-analytics.wixsite.com/statistics5
PY - 2019-01-01
AU - Malsiner-Walli, Gertraud
AU - Frühwirth-Schnatter, Sylvia
AU - Grün, Bettina
ER -