11. The Calibration module

Abstract

This chapter presents the features of the Calibration module of Uranie - version v4.11.0. The namespace of this library is URANIE::Calibration.

• 11.1. Introduction
  • 11.1.1. Distances and likelihoods used to compare observations and model predictions
• 11.2. Calibration classes, distance and likelihood functions, observations and model
  • 11.2.1. General introduction to data and model definition
  • 11.2.2. Defining data, distance and likelihood functions
    • 11.2.2.1. Recommended distance and likelihood functions, construction method
    • 11.2.2.2. Creating its own distance or likelihood function
    • 11.2.2.3. Available options for every distance and likelihood function
      • 11.2.2.3.1. Defining the variable weights
      • 11.2.2.3.2. Dumping all the estimates
      • 11.2.2.3.3. Defining the observations covariance matrix
      • 11.2.2.3.4. Specifying a Launcher object or options
      • 11.2.2.3.5. Dumping the distance details
  • 11.2.3. Common methods of the calibration classes
    • 11.2.3.1. Construction with a TRun
    • 11.2.3.2. Construction with a TCode
    • 11.2.3.3. Construction for a function with the Launcher architecture
    • 11.2.3.4. Running the estimate
    • 11.2.3.5. Estimate custom residuals
    • 11.2.3.6. Drawing the parameters
    • 11.2.3.7. Drawing the residuals
  • 11.2.4. Use-case for this chapter
• 11.3. Minimisation techniques
  • 11.3.1. Constructing the TMinimisation object
  • 11.3.2. Defining the TMinimisation properties
  • 11.3.3. Looking at the results
• 11.4. Analytical linear Bayesian estimation
  • 11.4.1. Constructing the TLinearBayesian object
  • 11.4.2. Defining the TLinearBayesian properties
  • 11.4.3. Looking at the results
    • 11.4.3.1. Transformation of the results
    • 11.4.3.2. Accessing the results
    • 11.4.3.3. Drawing the parameters
  • 11.4.4. Prediction of the variance
• 11.5. Approximate Bayesian Computation techniques (ABC)
  • 11.5.1. Constructing the TRejectionABC object
  • 11.5.2. Defining the TRejectionABC properties
    • 11.5.2.1. Defining the percentile
    • 11.5.2.2. Introducing noise for deterministic function
  • 11.5.3. Looking at the results
• 11.6. Markov chain Monte Carlo approach
  • 11.6.1. Constructing the TMCMC object
  • 11.6.2. Defining the TMCMC properties
    • 11.6.2.1. Chosing the MCMC algorithm
    • 11.6.2.2. Tuning the acceptation rate
    • 11.6.2.3. Information on the process
    • 11.6.2.4. Initialising several chains
    • 11.6.2.5. Initialising the process
  • 11.6.3. Running the estimate, exporting and loading chains, and continuing the calculation
  • 11.6.4. Investigating the quality of the samples through diagnostics and plots
    • 11.6.4.1. Drawing the trace
    • 11.6.4.2. Drawing the acceptation ratio
    • 11.6.4.3. Checking for convergence with the Gelman–Rubin diagnostic
    • 11.6.4.4. Thinning the chains with ESS
  • 11.6.5. Looking at the results
    • 11.6.5.1. Drawing the 2D trace
    • 11.6.5.2. Drawing the parameters
    • 11.6.5.3. Drawing the residuals
• 11.7. CIRCE method
  • 11.7.1. Constructing the TCirce object
  • 11.7.2. Defining the TCirce properties
  • 11.7.3. Running the estimate
  • 11.7.4. Looking at the results