Conceptuniform

Conceptuniform is a term used in theoretical modeling to describe a principle that assigns equal prior plausibility to all candidate concepts within a specified hypothesis space. It is used in cognitive science, machine learning, and information theory to minimize bias when the relative merits of concepts are not known a priori.

Formally, if the hypothesis space H contains N concepts, a conceptuniform prior sets P(h) = 1/N for all h in H. After observing data D, the posterior over concepts is P(h|D) ∝ P(D|h) P(h). This mirrors the standard Bayesian approach with a non-informative prior; the prior contributes maximal entropy.

Applications include modeling concept induction in which researchers avoid privileging any specific concept, and in unsupervised learning where a uniform prior across concepts can serve as a neutral baseline. It is also used in designing lexical concept inventories or feature mappings where prior knowledge is limited.

Limitations include the assumption of an equally plausible concept set, which is rarely realistic; uniform priors can hinder learning when the hypothesis space is large or structured, and determining an appropriate finite set H itself may be problematic. Computationally, summing over all h in H can be intractable.

See also: Bayesian inference, non-informative priors, maximum entropy, hypothesis space, concept learning.