In pymc, the way to do this is by defining a model using pm.Model(). You can define some distributions for your priors using pm.Uniform, pm.Normal, pm.Binomial, etc. To specify your likelihood, you can either specify it directly using pm.Potential (as I did above) if you have a closed form, otherwise you can specify a model based on your parameter using any of the distribution methods, providing the observed data using the observed argument. Finally, you can call pm.sample() to run the MCMC algorithm and get samples from the posterior distribution. You can then use arviz to analyze the results and get things like credible intervals, posterior means, etc.
Core Principle: openat(dir_fd, path)
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Query response time:
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