prior-elicitation

To incorporate domain knowledge into Bayesian priors, you can use expert elicitation workflows to translate subjective expertise into formal probability distributions, ensuring your Bayesian model robustly captures prior beliefs before observing data.

To constrain Bayesian priors to a specific probability range, you can find and fit distributions that satisfy specified bounds, ensuring parameters remain within desired limits like falling within a range of -2 to 2 with 95% probability.

To perform a sensitivity analysis on prior choices in Bayesian modeling, you assess the impact of different prior selections on your model conclusions, helping verify that results are driven by data rather than arbitrary prior specifications.

Yes, you need PreliZ and PyMC installed to use this prior elicitation workflow, as PreliZ provides the underlying distribution-fitting capabilities and PyMC handles the Bayesian model specification and sampling.

Yes, you can use expert elicitation to set priors for clinical trial data by capturing expert knowledge of a new drug's efficacy and translating it into constrained prior distributions for the treatment effect in your Bayesian model.