/mapping

name: mfe-mapping
description: "Functions, categories, and information. How mathematical objects relate to each other — morphisms, entropy, signal processing."
user-invocable: false
allowed-tools: Read Grep Glob
metadata:
  extensions:
    gsd-skill-creator:
      version: 1
      createdAt: "2026-02-26"
      triggers:
        intents:
          - "function"
          - "morphism"
          - "category"
          - "functor"
          - "information"
          - "entropy"
          - "signal"
          - "probability"
          - "transform"
        contexts:
          - "mathematical problem solving"
          - "math reasoning"

Mapping

Summary

**Mapping** (Part VII: Mapping) Chapters: 22, 23, 24, 25 Plane Position: (0.2, 0.4) radius 0.4 Primitives: 42

Functions, categories, and information. How mathematical objects relate to each other — morphisms, entropy, signal processing.

**Key Concepts:** Category, Probability Axioms, Functor, Natural Transformation, Shannon Entropy

Key Primitives

**Category** (definition): A category C consists of a collection of objects ob(C), a collection of morphisms hom(C) between objects, an identity morphism id_A for each object A, and a composition operation that is associative and respects identities.

• Analyzing structure-preserving maps between mathematical objects
• Identifying universal properties in algebraic structures
• Abstracting common patterns across different areas of mathematics

**Probability Axioms** (axiom): Kolmogorov's axioms: For a sample space Omega with sigma-algebra F, a probability measure P satisfies: (1) P(A) >= 0 for all A in F, (2) P(Omega) = 1, (3) P(union A_i) = sum P(A_i) for countably many disjoint events A_i.

• Formalizing uncertainty in mathematical models
• Defining the foundation for statistical inference
• Setting up probability spaces for random experiments

**Functor** (definition): A functor F: C -> D maps objects of C to objects of D and morphisms of C to morphisms of D, preserving identity morphisms F(id_A) = id_{F(A)} and composition F(g . f) = F(g) . F(f).

• Translating problems between different mathematical frameworks
• Identifying when a map preserves essential structure
• Building bridges between algebraic and geometric viewpoints

**Natural Transformation** (definition): A natural transformation eta: F => G between functors F, G: C -> D is a family of morphisms eta_A: F(A) -> G(A) for each object A in C, such that for every morphism f: A -> B in C, the diagram commutes: G(f) . eta_A = eta_B . F(f).

• Comparing two different ways to transform mathematical structures
• Establishing canonical relationships between functors
• Verifying that a family of maps is independent of arbitrary choices

**Shannon Entropy** (definition): For a discrete random variable X with probability mass function P(x), the Shannon entropy is H(X) = -sum_x P(x) log_2 P(x), measuring the average information content in bits per symbol.

• Measuring uncertainty or surprise in a random source
• Determining minimum bits needed to encode a message
• Quantifying information content of a probability distribution

**Fourier Transform** (definition): The Fourier transform of an integrable function f: R -> C is F{f}(xi) = integral_{-inf}^{inf} f(t) e^{-2pi i xi t} dt, mapping the function from the time domain to the frequency domain.

• Analyzing frequency content of signals
• Solving differential equations by transforming to the frequency domain
• Converting between time-domain and frequency-domain representations

**Random Variable** (definition): A random variable X: Omega -> R is a measurable function from the sample space to the real numbers, assigning a numerical value to each outcome. Its distribution is characterized by the CDF F_X(x) = P(X <= x).

• Modeling numerical outcomes of random experiments
• Defining probability distributions over numerical values
• Abstracting uncertain quantities for mathematical analysis

**Variance** (definition): The variance of a random variable X is Var(X) = E[(X - E[X])^2] = E[X^2] - (E[X])^2, measuring the expected squared deviation from the mean. Standard deviation sigma = sqrt(Var(X)).

• Quantifying the spread or dispersion of a distribution
• Assessing risk and uncertainty in financial and scientific models
• Comparing the reliability of different estimators

**Probability Distributions** (definition): A probability distribution assigns probabilities to events. Key distributions: Binomial B(n,p) with PMF C(n,k)p^k(1-p)^{n-k}; Poisson Poi(lambda) with PMF e^{-lambda}lambda^k/k!; Normal N(mu,sigma^2) with PDF (1/(sigma*sqrt(2pi)))exp(-(x-mu)^2/(2sigma^2)).

• Modeling specific types of random phenomena with parametric families
• Computing probabilities for binomial experiments, rare events, or measurement errors
• Selecting appropriate statistical models for data

**Joint Probability Distribution** (definition): The joint distribution of random variables X, Y is P(X in A, Y in B) for all measurable sets A, B. For continuous variables, the joint density f_{X,Y}(x,y) satisfies P(X in A, Y in B) = integral_A integral_B f(x,y) dy dx.

• Modeling relationships between multiple random variables
• Computing conditional distributions and independence properties

Composition Patterns

• Category + foundations-group-definition -> Group as a single-object category where all morphisms are invertible (parallel)
• Functor + mapping-functor -> Composite functor G . F: C -> E (sequential)
• Natural Transformation + mapping-functor -> Whiskered natural transformation (horizontal composition) (nested)
• Yoneda Lemma + mapping-category -> Embedding of any category into its presheaf category (Yoneda embedding) (sequential)
• Adjunction + foundations-group-definition -> Free-forgetful adjunction between groups and sets (parallel)
• Monad + mapping-category -> Kleisli category for sequential composition of effectful computations (sequential)

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