User guide

The guide documents how gpmp-contrib stores models, parameters, diagnostics, priors, and sequential-procedure state. The runnable Hartmann4 sequence is in Getting started.

Model construction and inspection

1. Concepts gives the package map and states which objects belong to gpmp and which objects are added by gpmp-contrib.

2. Models and computer experiments explains ComputerExperiment, ModelContainer, the provided Matérn classes, and the shape conventions for xi and zi.

3. Parameter selection explains what select_params does, how optimizer starts and bounds are chosen, and when ML, REML, or REMAP is used.

4. Model state and parameter objects shows where selected parameters, Param objects, optimizer reports, and resolved prior objects are stored.

5. Diagnostics and inspection explains the printed diagnosis and prediction-performance summaries.

Procedure-specific topics

• Priors for REMAP selection covers REMAP models with explicit prior anchors or hyperparameters.

• Sequential design, optimization, and set estimation covers expected improvement, excursion sets, set inversion, SMC particles, and BSS particles.

• Relaxed Gaussian processes covers the relaxed Gaussian-process procedure.

Examples and API links

The guide describes objects and stored state. Examples gives complete scripts, plots, and representative results. API reference gives signatures, shapes, return values, stored side effects, and failure conditions.

• Concepts
  • Object map
  • Common notation
  • Relation to gpmp
  • Package boundary
• Models and computer experiments
  • Shapes
  • ComputerExperiment
  • ModelContainer
  • GP defined by the provided Matérn classes
  • Posterior prediction
  • Mean and covariance specifications
  • Provided Matérn container classes
  • Constructor arguments
  • Minimal construction
  • Selected regularity
  • Related model topics
• Parameter selection
  • Covariance parameter vector
  • Which criterion is used
  • Maximum likelihood
  • Restricted likelihood
  • REMAP
  • What select_params does
  • Optimizer starts
  • Bounds and SciPy options
  • Stored results
  • Failure diagnosis
• Model state and parameter objects
  • Per-output state
  • Raw covariance parameters
  • Parameter objects
  • Bounds
  • Optimizer information
  • Prior objects
• Diagnostics and inspection
  • Model diagnosis
  • Prediction performance
  • Model state checks
  • Selection-criterion inspection
  • Posterior parameter sampling
• Priors for REMAP selection
  • Inspect and set priors
  • Available prior objects
  • Log-variance prior
  • Log-lengthscale prior
  • Resolution policy
  • Checks
• Sequential design, optimization, and set estimation
  • Base objects
  • Predictive quantities used by criteria
  • Expected improvement
  • Excursion sets
  • Set inversion
  • SMC and BSS particles
  • Which strategy to use
• Relaxed Gaussian processes
  • Problem setup
  • Optimization problem
  • Main functions
  • Threshold selection
  • Shapes
  • Example
  • Returned state
  • Failure conditions