Priorsia

Priorsia is a term used in discussions of probabilistic reasoning to describe a structured approach to encoding prior knowledge before observing data. In priorsia, prior information is broken into modular components such as priors, hyperpriors, and constraint rules, which can be combined and weighted to reflect different sources of domain expertise. This contrasts with traditional Bayesian practice where a single prior distribution is specified and then updated by likelihood through Bayes' rule.

The concept emphasizes transparency and auditability, enabling researchers to document the origins of assumptions, compare alternative knowledge bases, and perform sensitivity analyses. It is often associated with expert elicitation, where domain specialists contribute conditional beliefs, and with hierarchical or compositional models that organize knowledge across levels.

Applications span statistical modeling, machine learning, risk assessment, and decision support. In practice, priorsia can support more robust inference when data are sparse or noisy, by allowing priors to be structured to reflect known constraints, causal relationships, or resource limitations. It also aligns with ongoing efforts to combine symbolic knowledge with probabilistic reasoning.

Critics note that priorsia introduces subjectivity and potential elicitation bias, and that maintaining coherent, scalable priors across complex models can be challenging. As a coined term, priorsia appears primarily in speculative discussions and has not yet achieved wide consensus as a standardized methodology. Related topics include Bayesian inference, prior elicitation, hierarchical modeling, and probabilistic programming.