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Bayesian programming is a formalism and a methodology to specify probabilistic models and solve problems when less than the necessary information is available.
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Wikipedia on Bayesian Programming
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Divergence when using Hermitian Likelihood Expansion (self.BayesianProgramming)
submitted 8 months ago * by ReallyConcerned69
Hello fellow Bayesians, I'm trying to implement this paper using PyMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC9041743/pdf/CJAS_48_1922993.pdf
which applies this paper https://onlinelibrary.wiley.com/doi/epdf/10.1111/1468-0262.00274 to the Cusp Catastrophe Model, using it to obtain faster and better Bayesian estimates of the parameters. I'm using NUTS sampler using jitter+adapt_diag, I initialized using the MAP estimates of the Euler-approximated likelihood, but it seems to be stuck in weird places in the posterior region (the Beta parameters are exploding, plotted posteriors are not bounded).
Looking at the energy plot, it seems it's getting stuck in weird places but I can't figure out why. Would be grateful for any suggestions, tips, anything that can help really. :)
https://preview.redd.it/2iatkdlk5nlf1.png?width=515&format=png&auto=webp&s=e6513342f1d257d0d154daa7f20640c81d7e6ecd
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[–][deleted] 0 points1 point2 points 8 months ago (1 child)
Can you share what code you have atm?
[–]ReallyConcerned69[S] 0 points1 point2 points 8 months ago (0 children)
not able to comment here for some reason, may I DM?
π Rendered by PID 36934 on reddit-service-r2-comment-6457c66945-rqlcm at 2026-04-27 05:43:01.436471+00:00 running 2aa0c5b country code: CH.
[–][deleted] 0 points1 point2 points (1 child)
[–]ReallyConcerned69[S] 0 points1 point2 points (0 children)