Suppose there are some survival data and we want to fit a distribution on them using Bayesian semiparametric approach. Dirichlet process prior is useful in this method. after computing conditional posterior distribution for all unknown parameters I want to simulating data from dirichlet process using Gibbs sampler method. now how can I do these?
I found JSTOR has a lot of these 70s and 80s references that have kernel based pricing from Financial markets. Basic operations of Dirichlet are easiest done using Matlab, as also for Kalman filters
Also many of the kernel pricing approaches are from European Journals/Authors
Gibbs sampler is used to estimate the posterior distributions of all known parameters. It seems that you want to sample from the data model. Based on the obtained posterior mixture model with known number of mixtures and corresponding parameters, the data model is now a finite mixture model, and then you can simply sample desired new data, all of which will follow the same density function.