TYCHE

TYCHE is Babadook's probability, mathematical-statistics and stochastic-inference core. It begins before an estimator runs: with the population, observation process, estimand, assumptions and interpretation the result must answer.

A probability is never published as an isolated scalar. Forecasts retain their issue-time information. Intervals retain their coverage or posterior meaning. Monte Carlo work retains stream identity, diagnostics and approximation evidence. A numerically correct result can still be denied for the interpretation a consumer requested.

Products consume bounded TYCHE results for uncertainty, forecasting, surveillance, risk and experimental evidence. They remain responsible for domain truth, causal claims, decisions and action.

TYCHE does not decide what a business, mission or clinician should do. It does not own causal identification, source-world truth, chronology or generic numerical execution. It makes probabilistic and statistical meaning explicit enough that another product can use the result without claiming more than the evidence supports.

Typed Probability and Observation Semantics

Models probability, observations, samples, measures, conditioning information and support as different governed objects rather than unlabeled scalar values.

Estimand and Estimator Discipline

Defines the target quantity before selecting a procedure and rejects substitutions that silently change population, weighting, time basis or missing-data assumptions.

Frequentist, Bayesian and Sequential Inference

Keeps likelihoods, posteriors, confidence procedures, credible regions, prediction intervals and sequential evidence in their own result classes.

Reproducible Monte Carlo and Approximate Inference

Runs Monte Carlo, quasi-Monte Carlo, multilevel, rare-event, MCMC, SMC and approximate inference with explicit random-stream and approximation evidence.

Temporal, Spatial and Stochastic-Process Models

Supports forecasting, spatial statistics, point processes, random fields, stochastic processes and stopping-time questions without collapsing chronology into statistical law.

Measurement Uncertainty and Calibration

Carries measurand, calibration, covariance, traceability, influence quantities and approximation limits with a measurement result.

Statistical Definition Kits and Method Registry

Lets teams author, validate, compile, publish, migrate and retire probability laws, estimators, tests, processes and domain packs as versioned definitions.

Scientific Interfaces and Artifact Interchange

Projects the same statistical contract through gRPC, REST, native and generated SDKs, CLI, streaming, WASM and scientific artifact formats.

Robust and Nonparametric Statistics

Analyzes contaminated, heavy-tailed, distribution-uncertain and rank-structured data without pretending classical regularity holds.

Experimental Design, Power and Sequential Learning

Constructs randomized, blocked, adaptive and sequential designs with explicit estimands, stopping rules, allocation and operating characteristics.

Dependence, Information and High-Dimensional Structure

Represents copulas, covariance, entropy, divergence, graphical dependence and structured high-dimensional statistical relationships.

Deterministic Random-Stream Identity

Creates reproducible, separated random streams and substreams across chains, particles, replications, scenarios and distributed workers.

Statistical Result Provenance and Currentness

Tracks the complete path from observations and definitions through computation, interpretation, correction and consumer use.

Products powered by TYCHE

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TYCHE

Products consume bounded TYCHE results for uncertainty, forecasting, surveillance, risk and experimental evidence. They remain responsible for domain truth, causal claims, decisions and action.