IndisputableMonolith.Information.ShannonEntropy
ShannonEntropy module equips Recognition Science with discrete probability distributions and Shannon entropy expressed as J-cost. Researchers deriving compression and error-correction bounds cite these primitives. The module supplies type definitions for ProbDist, entropy functions, non-negativity results, and direct equality to total J-cost, built from imported Cost definitions.
claimLet $P=(p_1,…,p_n)$ be a discrete probability distribution with $p_i≥0$ and $∑p_i=1$. Shannon entropy is $H(P)=-∑p_i log_2 p_i$, shown equal to the total J-cost of $P$.
background
The module sits in the Information domain and imports the RS time quantum τ₀=1 tick together with J-cost primitives. It defines ProbDist as a discrete probability distribution over n outcomes, introduces shannonEntropy, surprisal, and the relation shannon_equals_jcost. Additional results cover entropy non-negativity, maximality for the uniform distribution, and zero entropy for deterministic cases, all expressed in RS-native units.
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
Definitions here supply the information measures required by the Compression module for INFO-003 (data compression limits from J-cost and Shannon source coding), by ErrorCorrectionBounds for INFO-005 (8-tick error correction), and by InformationIsLedger for IC-001 (information as the physical ledger).
scope and limits
- Does not treat continuous distributions.
- Does not derive the source coding theorem.
- Does not address quantum or mutual information.
- Assumes finite discrete supports only.
used by (3)
depends on (2)
declarations in this module (19)
-
structure
ProbDist -
def
uniform -
def
shannonEntropy -
theorem
entropy_nonneg -
theorem
max_entropy_uniform -
theorem
zero_entropy_deterministic -
def
probJCost -
theorem
jcost_uniform -
def
totalJCost -
theorem
shannon_equals_jcost -
def
surprisal -
theorem
entropy_is_expected_surprisal -
theorem
entropy_from_recognition_cost -
theorem
source_coding_theorem -
def
entropyApplications -
def
boltzmannFactor -
theorem
thermodynamic_entropy_connection -
structure
ShannonFalsifier -
def
experimentalStatus