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Finite-Alphabet Entropy and Divergence — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Convexity
- Countability and Uncountability
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability Spaces and Random Variables
- Finite-Alphabet Entropy and Divergence
- Foundations of the Real Numbers for Analysis
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Order, Zorn's Lemma, and the Axiom of Choice
- Power Series and Real-Analytic Functions
- Probability Spaces Random Variables and Expectation
- Properties of the Integral and the Working FTC
- Quantum Communication and Fingerprinting
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Logarithm and General Powers
- The Riemann Integral: Definition and Integrability
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
These worked finite cases verify the binary entropy maximum and endpoint values, the zero information of independent variables, the entropy of a deterministic output, and the direction and support behavior of finite KL divergence. The examples state their probability vectors and calculate each value using the same zero and support conventions as the A page.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Binary entropy and its extremizers
Example
For a Bernoulli law , the entropy in nats is It is zero exactly at and has unique maximum at .
Facts & Assumptions
Given: and the Bernoulli probability vector .
Finite distributions, entropy, divergence, and stochastic matrices: , with zero terms set to .
Finite KL divergence is nonnegative with equality exactly at equal laws: for finite probability vectors, , with equality iff .
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: is strictly increasing and .
Verification
Substituting into [F1] gives the displayed formula. If or , the nonzero coordinate is and [F3] gives , so .
Let . The finite KL formula gives , also at because zero terms are omitted. By [F2], this value is nonnegative and vanishes exactly when , i.e. . Thus with a unique maximizer.
If , then and by [F3], so both terms in are positive. Along with step 1.1 this proves that the only zeros are the endpoints.
Finite KL divergence is directional and may be infinite
Statement refuted
For every pair of finite probability vectors on the same alphabet, .
Facts & Assumptions
Given: The finite KL convention and .
Finite distributions, entropy, divergence, and stochastic matrices: , with if for some coordinate.
Finite KL divergence is nonnegative with equality exactly at equal laws: finite KL is nonnegative and is zero exactly for equal probability vectors.
Counterexample
Given: Take the alphabet , , , and .
The support of is contained in that of because . By [F1], , a finite real number. Since , [F2] makes this value strictly positive, consistent with the formula.
The second coordinate has . Thus [F1] gives . The two directions are unequal, which refutes the statement.
For the separate disjoint point masses and , coordinate has and coordinate has ; applying [F1] in each direction gives . This records the disjoint-support boundary.
Independent and deterministic finite information
Example
For finite , independence gives . If for a function on the finite alphabet of , then .
Facts & Assumptions
Given: Finite with joint pmf ; in the second case .
Finite mutual information: is the finite KL divergence between and .
Finite conditional entropy: conditional entropy is the average of the entropies of the conditional pmfs.
Verification
If are independent, then at every coordinate. Each positive-support ratio in [F1] is , so every summand is zero and .
If , then for each with , has mass at and elsewhere, so [F4] gives entropy . Averaging these conditional entropies by [F3] gives , and [F2] yields .