Travis A Jefferies

pymetalog. Public repo for the pymetalog project

metalog_jax. Traditional probability distributions often fail to model real-world data. You end up trying multiple distributions, truncating to enforce bounds, using hard-to-interpret mixture models, or writing custom likelihood functions. What if one distribution family could fit virtually any continuous data?