Another question regarding the mixture distribution. Suppose a random variable (Y) follows a mixed normal distribution with a certain weight (p) for each component. You can always rewrite out this model into a hierarchical model by introducing a latent guy (Z) while Z represents the component of mixture. Therefore, the probability of Z equals to the weight of each component in the mixture. (See Chapter 24 of 'Markov Chain Monte Carlo in Practice'--- Richardson & Spiegelhalter for detailed discussion.) My question is that during the Gibbs sampling, if the weight of a certain component (i) is extremely small, say around .002, the chance of Z being sampled is very small. You may run into the situation that Z takes any values but i after a round of Gibbs sampling, this causes problem in next round of simulation. Who can tell me how to handle this situation? Any suggestions are welcome.
Many thanks, Nicole . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
