inferable

Inferable is an adjective used to describe something that can be inferred, deduced, or derived from evidence, rules, or a formal system. It is used across philosophy, logic, statistics, and computer science to indicate that a conclusion, property, or parameter can be obtained through reasoning or data.

In logic and philosophy, a statement is inferable from a theory if there exists a valid sequence of inferences from the theory’s axioms or premises that yields the statement. Semantically, a conclusion may be inferable with respect to a model, while provability concerns formal derivations within a deductive system. These concepts often interact but are not always interchangeable, depending on the logic in use.

In statistics and related fields, inferability often relates to identifiability: a parameter is inferable if its value can be uniquely determined from the probability distribution of observed data. Models with non-identifiable parameters limit practical inferability, as multiple parameter configurations can produce indistinguishable data.

In machine learning and data science, inferability describes the extent to which data allow reliable estimation or decoding of latent variables, structure, or causal relationships. Techniques aim to maximize inferability by choosing interpretable models, improving data quality, and reducing ambiguity in the mapping from data to conclusions.

See also: inference, identifiability, provability, deductive reasoning.