TY - UNPB

T1 - Proximity penalty priors for Bayesian mixture models

AU - Sperrin, Matthew

N1 - 14 pages, 6 figures

PY - 2011/7/27

Y1 - 2011/7/27

N2 - When using mixture models it may be the case that the modeller has a-priori beliefs or desires about what the components of the mixture should represent. For example, if a mixture of normal densities is to be fitted to some data, it may be desirable for components to focus on capturing differences in location rather than scale. We introduce a framework called proximity penalty priors (PPPs) that allows this preference to be made explicit in the prior information. The approach is scale-free and imposes minimal restrictions on the posterior; in particular no arbitrary thresholds need to be set. We show the theoretical validity of the approach, and demonstrate the effects of using PPPs on posterior distributions with simulated and real data.

AB - When using mixture models it may be the case that the modeller has a-priori beliefs or desires about what the components of the mixture should represent. For example, if a mixture of normal densities is to be fitted to some data, it may be desirable for components to focus on capturing differences in location rather than scale. We introduce a framework called proximity penalty priors (PPPs) that allows this preference to be made explicit in the prior information. The approach is scale-free and imposes minimal restrictions on the posterior; in particular no arbitrary thresholds need to be set. We show the theoretical validity of the approach, and demonstrate the effects of using PPPs on posterior distributions with simulated and real data.

KW - stat.ME

M3 - Working paper

BT - Proximity penalty priors for Bayesian mixture models

ER -