================================================================== The gateway between this list and the sci.stat.edu newsgroup will be disabled on June 9. This list will be discontinued on June 21. Subscribe to the new list EDSTAT-L at Penn State using the web interface at http://lists.psu.edu/archives/edstat-l.html. ================================================================== . Hi,
I have a computed numerical bayesian posterior distribution (sampled with MCMC), which consists of 10^7 points in 10-d space. The distribution is fairly multimodal. I have been thinking about using the k-means algorithm for extracting the point density peaks from this data. There is a nice variant called G-means [1], which looks attractive. I have also considered using SOM, but it seems to lack proper mathematical justification for this task. Are there any other alternatives? What do people usually use to model a sampled multimodal posterior distribution? Hamerly et al., "Learning the k in k-means", http://www.cs.ucsd.edu/Dienst/UI/2.0/Describe/ncstrl.ucsd_cse/CS2002-0716 juha
