Has anyone tried reproducing these distributions in pymc3?

**"A statistical model for the analysis of beta values in DNA methylation studies"**

I have just tried, and although I can get the right PDF, with regular numpy, I have failed to implement it as a pymc3 distribution to do Bayes inference.

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To give you more context, I have recently read this preprint: **Profiling epigenetic age in single cells**

The authors use a probabilistic approach to predict the age of single cells based on their DNAm profiles. Aging clocks are nothing new for bulk samples, but it is the first time I see smt like this done on single cells.

So, I had a reasonable question: can CpG ß-values be modeled with a normal distribution? The authors do not discuss much, what assumptions they make to make it all work, but imo, it is implied that DNAm is treated as a Bernoulli trial, which is then aggregated into a normally distributed ß-value of a CpG site.

Then it turned out that there is a specific PDF associated with ß-values. Certainly, I was curious to implement it and eventually failed.

I am currently using truncated normals, as implemented in pymc3 to work with ß-values. I wonder if switching for the distribution from the first article would give significantly different results.