Best example tutorial for fitting 2D histogram data?


I have 2D histogram data and a generative model for predicting 2D histograms that can be compared to the observed 2D histogram. I would like to use PyMC to fit the model’s parameters to best fit the observed 2D histogram (in a Bayesian way – so define priors and a likelihood and get posteriors on the parameters). I think this should be pretty straightforward with PyMC but I wanted to ask – which of the example PyMC tutorials would be most applicable and straightforward for adapting to my use case? From here: PyMC Example Gallery — PyMC example gallery (I am new to PyMC.)


Perhaps this one: Modeling spatial point patterns with a marked log-Gaussian Cox process — PyMC example gallery

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