podobnos

Podobnos is a term used in statistics and data science to denote a family of similarity measures between two probability distributions or between two feature vectors. It is not a single fixed metric but a framework in which different per-feature similarity choices can be combined to yield an overall similarity score. The name suggests a notion of likeness or resemblance and is sometimes encountered in discussions of comparative data analysis where exact probabilities are less important than the degree of agreement between two representations.

In its simplest form, if P and Q are distributions over N features with probabilities p_i and q_i, and if s: [0,1] × [0,1] → [0,1] is a local similarity function, then Podobnos(P, Q) can be defined as an average of per-feature similarities: Podobnos(P, Q) = (1/N) ∑_{i=1}^N s(p_i, q_i). Common instantiations of s include s(u,v) = u v + (1−u)(1−v) (probability of agreement) or s(u,v) = 1 − |u − v| (linear closeness). Kernel-based or overlap-based variants are also used, yielding different sensitivity to distributional shifts.

Computation typically relies on estimating the marginal probabilities from data, possibly via histograms, kernel density estimates, or sampling, followed by the chosen per-feature similarity. Podobnos is used in clustering, anomaly detection, model comparison, and recommender systems, where a robust, interpretable measure of likeness between distributions or feature representations is valuable. Limitations include dependence on feature scaling, choice of s, and the challenge of comparing distributions with differing supports.

See also: similarity measures, distance metrics, KL divergence, cosine similarity, maximum mean discrepancy. The term derives from a linguistic root related to likeness.