Module active_learning 

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

Re-exports

pub use acquisition::RecommendedSample;

pub use noise_floor::detect_noise_floor;

pub use noise_floor::detect_noise_floor_default;

pub use noise_floor::NoiseFloorConfig;

pub use noise_floor::NoiseFloorDiagnostic;

pub use orchestration::CalibrationSession;

pub use orchestration::IterationOutcome;

pub use orchestration::PrecisionRecord;

pub use orchestration::SampleRecord;

pub use orchestration::SessionConfig;

pub use precision::assess_precision;

pub use precision::percentile;

pub use precision::PrecisionAssessment;

pub use precision::PrecisionStatus;

pub use variance::posterior_std;

pub use variance::posterior_std_grid;

pub use variance::posterior_variance;

Modules

acquisition

Algorithm 2: Variance-Maximizing Acquisition Function

multi_sensor

Multi-sensor orchestration support.

noise_floor

Algorithm 4: Noise Floor Detection

orchestration

Algorithm 5 / T2.2: Calibration Orchestration Loop

precision

Algorithm 3: Precision Assessment

variance

Algorithm 1: Posterior Variance Computation