I’ve been reading papers by Emily Fox’s group on multiple time series models with correlations across time and space. Some examples are:
Spatio-Temporal Low Count Processes with Application to Violent Crime Events
Modeling the Complex Dynamics and Changing Correlations of Epileptic Events
In each of these papers - and in almost every paper I’ve read that uses a bayesian approach - the authors define precisely the sampling steps for performing inference. E.g., this is from the 2nd paper linked above:
If I were to implement one of these models in pymc3 and just hit “the inference button,” would that provide an accurate sampling from the posterior?
If not, why not? Is there a way I can perform inference on one of these models in pymc3?
If yes, why do authors always specify the sampling algorithm?
If it depends on the model, are there some rules that determine when we can just hit “the inference button” and when we can’t?
My questions above are just about accuracy - what about speed?
I realize this may be a naive and/or meaty question - thanks in advance for replies.