Fasmarginal

Fasmarginal is a neologism that has appeared in niche academic discussions and online glossaries to denote a family of ideas at the intersection of fast computation and marginal analysis. There is no single, widely accepted definition, and the term can refer to different approaches in different fields.

Usage in statistics and machine learning: Some writers use fasmarginal to describe fast approximate methods for estimating marginal effects in statistical models, where the marginalization over certain variables is carried out with simplified or heuristic computations to reduce runtime. In Bayesian contexts, it has been used to describe lightweight marginalization techniques that preserve key posterior properties while trading exactness for speed.

Usage in real-time systems: In real-time decision systems and simulations, fasmarginal has been used to denote strategies that repeatedly marginalize over unlikely branches or low-impact states to keep decision latency low.

Origin and etymology: The term appears to be a portmanteau of fast/fast approximation and marginal, drawing on the concept of marginalization in probability and decision theory. Its exact origin is uncertain and spelling conventions vary.

Relation to established concepts: Fasmarginal is related to marginal distributions and marginal effects, and to approximate inference methods such as variational inference and Monte Carlo approximations. However, it is not a standard term in any major field.

Reception: Because the term lacks a formal definition, interpretations vary. Some researchers view it as a descriptive umbrella for several fast, approximate marginalization methods; others warn that the lack of precision can obscure methodological distinctions.

See also: Marginal distribution, Marginal effect, Approximate inference, Real-time computation.