Probentyps

Probentyps is a framework used in probability theory and statistics to categorize probabilistic constructs by the underlying type of probabilistic reasoning they embody. The term is used in theoretical discussions and in some software documentation related to probabilistic programming and uncertainty quantification. It aims to clarify how probabilities are generated, interpreted, and updated as new information becomes available.

The typology centers on three principal probentyps. Aleatoric-type probentyps model uncertainty that is inherent to the process being studied; their probabilities reflect irreducible randomness and cannot be fully resolved by collecting more data, though measurement error can be mitigated. Epistemic-type probentyps represent uncertainty arising from incomplete knowledge, model misspecification, or limited data; in these cases probabilities reflect beliefs that can be revised through evidence, learning, or model improvement. Hybrid-type probentyps combine both components, attributing portions of total uncertainty to irreducible variability and to gaps in knowledge, enabling partial learning from data while preserving irreducible randomness where appropriate.

Relation to other concepts: Probentyps complements traditional uncertainty taxonomy by emphasizing the source and evolution of probability assignments rather than solely their numerical form. In practice, they are used in probabilistic programming, risk analysis, model auditing, and decision-support systems to separate sources of uncertainty and to guide data collection and model refinement.

Criticisms include that the boundaries between probentyps can be blurry in real-world models and that classifications may depend on model structure and assumptions. Despite this, the framework provides a lens for analyzing how uncertainty is handled across different modeling contexts.