frameworkDecisions and computation

Information theory

Quantify uncertainty, coding cost, and predictive information without confusing compression with understanding.

Working definition

Information theory measures uncertainty and dependence through quantities such as entropy, cross-entropy, and mutual information. Estimates depend on distributions, sample size, discretization, and conditioning; apparent information can arise from leakage or shared trends.

Notation

H(X) = −Σ p(x) log p(x)I(X;Y) = H(Y) − H(Y|X)

Assumptions

  • Probability distributions are defined.
  • Sampling supports the estimator.
  • Conditioning variables prevent obvious confounding where possible.

Invariants

  • Entropy is nonnegative for discrete variables.
  • Mutual information is symmetric and nonnegative.
  • Deterministic invertible recoding preserves information.

Reproducible procedure

  • Define variables and estimation method.
  • Estimate against shuffled and simple baselines.
  • Use held-out data and report estimator bias.

Error and boundary controls

  • High-dimensional estimates are sample hungry.
  • Binning changes estimates.
  • Mutual information does not identify causal direction.

What this does not establish

Statistical dependence between planetary features and outcomes does not establish a causal celestial mechanism or robust future utility.

Explicit applications

1 cross-domain bridges

Astrology traditionsempirical test

Incremental rule information audit

Measure whether a rule pack adds out-of-sample information beyond base rates and ordinary covariates.

Inputs

  • locked rule features
  • outcomes
  • baseline predictions

Outputs

  • incremental information
  • uncertainty
  • null comparison

Transformation: Estimate held-out score or information gain with leakage controls.

Limit: Dependence does not identify cause and must replicate prospectively.

Open connected system →

Authoritative references

  1. [1]NIST/SEMATECH e-Handbook of Statistical Methods · National Institute of Standards and Technology

    Establishes: Methods for uncertainty analysis, calibration, time-series modeling, process monitoring, experimental design, reliability, and statistical comparison.

    Boundary: Statistical procedures quantify evidence under a design and model; they do not repair biased sampling, outcome leakage, post-hoc hypotheses, or unmeasured confounding.

Related mathematical concepts