Closed julienmalard closed 6 years ago
PyMC2 is no longer being actively developed, though you are welcome to submit a pull request. I would strongly recommend looking at PyMC3, as the Hamiltonian MC samplers (particularly NUTS) are far more effective than adaptive Metropolis.
Thank you! I am mainly dealing with a relatively slow external model (1-2 seconds to evaluate, including reading output; https://github.com/julienmalard/Tikon/). Am I correct in understanding that NUTS would not be a possible MC sampler for this case? (And if not, would you have any recommendations?) I have been seriously considering porting to PyMC3, but wanted to understand better what the performance improvement might be before dedicating myself to it.
Many thanks, Julien Malard
When trying to fit a model with an external function, the external function will not rerun at each iteration if the variable list (l_var_paráms) includes a degenerate likelihood or a uniform likelihood with upper==lower.