A stupid question: what is log_ps compared to the number of identical samples in the output ?
If Ni counts the number (frequency) of identical samples for each unique parameter set i, I expect that exp(log_ps) is the probably to find sample i in the output, and that is is equal to Ni (once normalized, and after burning half of the samples). Is it correct ?
Does it means that the convergence is not reached ? I have to admin that my GR stats are close to 1.2 but some are above.
The correlated question is how to I select the "best" parameter set. Should I take that with max(log_ps) or the most frequent in the sampled set ?
A stupid question: what is log_ps compared to the number of identical samples in the output ?
If Ni counts the number (frequency) of identical samples for each unique parameter set i, I expect that exp(log_ps) is the probably to find sample i in the output, and that is is equal to Ni (once normalized, and after burning half of the samples). Is it correct ?
Does it means that the convergence is not reached ? I have to admin that my GR stats are close to 1.2 but some are above.
The correlated question is how to I select the "best" parameter set. Should I take that with max(log_ps) or the most frequent in the sampled set ?