CPMaps

CPMaps is an acronym used in several technical domains to denote map-like data structures that attach additional information to keys. There is no single canonical definition; the interpretation depends on the field and context.

In probabilistic reasoning and probabilistic programming, CPMaps often refer to conditional probability maps, where each key maps to a probability distribution conditioned on context or input variables. They enable representation of conditional beliefs, support combination of information from multiple sources, and facilitate operations such as marginalization and inference.

In constraint programming and optimization, CPMaps can denote constraint-propagation maps. These structures store variables with their domains and provide propagation rules to prune values, maintain consistency, and support constraint posting and retraction during solving processes.

In computer graphics and geometric modelling, CPMaps can denote control-point maps, associating keys (such as parameter values) with control points used in splines, deformations, or texture mapping. Such maps help manage how a shape or image is driven by a compact set of control points.

Common operations across these variants include insertion, lookup, merging or composition of maps, and domain-specific procedures like conditioning, propagation, or resampling. Implementations vary: hashmap-based CPMaps offer fast average-case lookups, while tree-based or compressed representations can provide ordered iteration or memory efficiency.

The term CPMaps is largely contextual and is most informative when clarified by the relevant domain. See also maps, probability distributions, constraint propagation, and control points.