Reference Guide

There are two kinds of documentation for BEST. The reference guide you are reading right now and the docstrings of the actual Python code. In this reference guide, we have omitted the details of the various objects or functions. We only describe how the should be used. For the complete details you are advised to look at the code itself.

The main module of BEST is (of course) called best.

The BEST package is split in several submodules:

• Core
• Basic Objects
• Miscellaneous
• Linear Algebra
  • Manipulating Kronecker Products
  • Generalized Singular Value Decomposition
  • Incomplete Cholesky Decomposition
• Serialization
• Maps
  • The basic concepts
  • The best.maps.Function class.
  • Some Examples
  • Screened Function
  • Basis
• Covariance Functions
  • The best.maps.CovarianceFunction class
  • Examples of Covariance Functions
  • Constructing Basis from Covariance Functions
• Domain
• Random
  • Conditional Random Variable
  • Random Vector
  • Random Vector of Independent Variables
  • Karhunen-Loeve Expansion
  • Likelihood Functions
  • Distributions
  • Sampling Distributions
  • Mixture of Distributions
  • Posterior Distribution
  • Markov Chain Monte Carlo
  • Simple MCMC Example
  • Sequential Monte Carlo
  • Simple Sequential Monte Carlo Example
• Generalized Polynomial Chaos
  • 1D Orthogonal Polynomials
  • Product Basis
  • Constructing Polynomials
• Generalized Linear Model
• Relevance Vector Machine
  • A Simple 1D Example
• Gaussian Process Regression
  • Multi-output Separable Gaussian Process Regression
  • A simple 1D example
  • Treed Gaussian Process Regression
  • Simple Treed Gaussian Process Regression Example
• Design of Experiments
  • Latin Hyper-cube Designs
• Inverse Problems