It’s unusual to have a zero-inflated continuous distribution (e.g. normal). However, pymc3 provides a number of zero-inflated discrete distributions (Poisson, Binomial, Neg. Binomial), which might be suitable to your data, based on the figure you provided. If your data is continuous, you could transform it by binning and scaling it to the naturals.