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#65 PyMC, Aeppl, & Aesara: the new cool kids on the block, with Ricardo Vieira

Learning Bayesian Statistics

CHAPTER

How to Write Different Models for Different Processes

The idea is not to only do that on the non centr ation. T would be to mi be able to use other repatrizations. So if you have a complex likelihood or a unobserved equivalent variable, sometimes you would write it differently. Sometimes you might write the model differently depending on whether you are conditioning. The censor distribution, which introduces, is very interesting in that you cannot really do nut sampling with it if it's not observed. And user wouldn't need to specify different models depending on what they are doing.

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