///////////////////////////////////////////////////////////////////////////////////////////////////
// enum eDreamInputPar
[helpstring("DREAM Input parameters")]
typedef enum eDreamInputPar
{
[helpstring("Dimensionality target distribution d")]
eParProblemDimension,
[helpstring("Number of Markov chains N")]
eParNumberOfMarkovChains,
[helpstring("Number of generations T")]
eParNumberOfGenerations,
[helpstring("Choice of likelihood function")]
eParLikelihoodChoice,
[helpstring("Number of crossover values nCR")]
eParNumberOfCrossoverValues,
[helpstring("Number chain pairs for proposal delta")]
eParNumberChainPairs,
[helpstring("Random error for ergodicity lambda")]
eParRandomErrorForErgodicity,
[helpstring("Randomization zeta")]
eParRandomization,
[helpstring("Test to detect outlier chains")]
eParOutlierTest,
[helpstring("Probability of jump rate of 1 pJumpRate_one")]
eParProbabilityOfJumprate,
[helpstring("Adapt selection prob. crossover pCR")]
eParAdaptSelection,
[helpstring("Each Tth sample is stored thinning")]
eParThinnigSampleToStore,
[helpstring("GLUE likelihood parameter")]
eParGlueLikelihood,
[helpstring("Diagnostic Bayes? Values: 1=true, 0=false")]
eParBayesDiagnostic,
[helpstring("Scaling factor of built-in jump rate beta0")]
eParScalingFactorOfJumpRate,
[helpstring("Bayesian Model Averaging (BMA) with hydrologic data. Number of different models")]
eParNumberOfDifferentModelsBMA,
[helpstring("Model vectorization type")]
eParVectorization,
[helpstring("Number of steps after which the convergence is checked and updated")]
eParSteps,
[helpstring("Initial sampling distribution type")]
eParInitialDistribution,
[helpstring("Boundary handling type")]
eParBoundHandling,
[helpstring("Use prior distribution even for m_InitialDistribution != eInitPrior? Values: 1/0")]
eParUsePriorDistribution,
[helpstring("Prior distribution type")]
eParPriorDistributionType,
[helpstring("Evaluator can run in parallel?")]
eParParallelMode,
[helpstring("Dummy: number if input parameters")]
eParMax,
} eDreamInputPar;