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§Probability and Stochastic Distribution Suite (scicdh::probability)
This module provides foundational discrete probability distributions, combinatorial
calculation architectures, and joint random vector analysis engines mapped entirely
onto f64 scalar float values.
§Core Architecture Components
- Combinatorics Engines: Safe, bounded definitions for factorials ($n!$), permutations ($P_r^n$), and combinations ($C_r^n$).
- Random Vector Framework: Bivariate discrete spaces executing cross-joint tracking, marginal breakdowns, and conditional expectation parameters.
- Discrete Distributions: Built-in calculation matrices for Binomial, Geometric, Pascal, and Hypergeometric models.
Structs§
- Binomial
- Ingests context tracking parameters mapping independent Binomial distribution configurations.
- Geometric
- Tracks observations processing structural patterns for Discrete Geometric configurations.
- Hyper
Geometric - Tracks parameters mapping states across Hypergeometric sample space distributions where sampling occurs without replacement.
- Joint
Probability - A collection type wrapper containing a two-dimensional grid layout of raw statistical densities.
- Pascal
- Handles Negative Binomial (Pascal) distributions tracking total required iterations up until target success thresholds are passed.
- Probability
- Placeholder configuration struct representing the abstract probability execution namespace context.
- Random
Variable - Represents a univariate discrete Random Variable containing mapped outcomes and matching probability mass profiles.
- Random
Vector - A multivariate structure tracking pairs or collections of intersecting random processes over a discrete matrix space.
Functions§
- check_
probability - Runs telemetry diagnostic logging verifications across your module distribution functions to confirm precision alignments.
Type Aliases§
- CDHResult
- Result Type for the library