blr-active 0.1.0

Active learning orchestration for Bayesian Linear Regression with Automatic Relevance Determination
Documentation 
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//! Active learning module for precision-driven sensor calibration.
//!
//! Implements Algorithms 1–5 from Plan 03: online active learning that guides
//! users to measure where model uncertainty is highest, detecting noise floors,
//! and iterating until a user-specified precision goal is reached.
//!
//! # Module Structure
//!
//! - [`variance`] — Algorithm 1: posterior variance computation
//! - [`acquisition`] — Algorithm 2: variance-maximizing acquisition function
//! - [`precision`] — Algorithm 3: precision assessment and goal checking
//! - [`noise_floor`] — Algorithm 4: noise floor (plateau) detection
//! - [`orchestration`] — Algorithm 5: synchronous calibration state machine
//! - [`multi_sensor`] — multi-sensor session management
pub mod acquisition;
pub mod multi_sensor;
pub mod noise_floor;
pub mod orchestration;
pub mod precision;
pub mod variance;

// Convenience re-exports for the most commonly used items
pub use acquisition::RecommendedSample;
pub use noise_floor::{
    detect_noise_floor, detect_noise_floor_default, NoiseFloorConfig, NoiseFloorDiagnostic,
};
pub use orchestration::{
    CalibrationSession, IterationOutcome, PrecisionRecord, SampleRecord, SessionConfig,
};
pub use precision::{assess_precision, percentile, PrecisionAssessment, PrecisionStatus};
pub use variance::{posterior_std, posterior_std_grid, posterior_variance};