Proximity penalty priors for Bayesian mixture models

Details Proximity penalty priors for Bayesian mixture models Proximity penalty priors for Bayesian mixture models

2011-07-27

arxiv.org/abs/1107.5508v1

Statistics

Methodology

14 pages, 6 figures

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Proximity penalty priors for Bayesian mixture models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Proximity penalty priors for Bayesian mixture models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Proximity penalty priors for Bayesian mixture models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-113546