Bayesian Modeling: Estimate a Computer Model and Make Uncertain Predictions
Add double quotes to a string or to each element of a string vector
Metropolis Rule
Run BaM
Bloc-diagonal matrix constructor
Check a formula make sense within a given namespace
dataset object constructor.
densityPlot
BaM downloader
Was BaM executable found?
Bathymetry interpreter
BaM catalogue
Get Inference Components
Get initial values
Get object names
Get parameter names
dataset tester
mcmcCooking tester
mcmcOptions tester
mcmcSummary tester
model tester
parameter_VAR tester
parameter tester
prediction tester
remnantErrorModel tester
residualOptions tester
runOptions tester
xtraModelInfo tester
Log-likelihood function: iid Gaussian
Log-likelihood function: independent-linear Gaussian
BaM log-likelihood
BaM log-posterior
BaM log-posterior
Log-prior function: improper flat prior
Adaptive Metropolis sampler
Adaptive One-At-A-Time Metropolis sampler
mcmcCooking constructor.
mcmcOptions object constructor.
mcmcSummary constructor.
model object constructor.
Varying parameter object constructor.
parameter object constructor.
prediction object constructor.
Write a character vector (typically output of toString() to file)
MCMC Reader
remnantErrorModel object constructor.
residualOptions constructor.
Run BaM executable
Run Model
runOptions constructor.
Set initial values
Path to BaM
Estimation of a BaRatin-SPD model
Properties of VAR parameters
Write value-comment pairs into string vector
dataset to string
mcmcCooking to string
mcmcOptions to string
mcmcSummary to string
model to string
parameter_VAR to string
parameter to string
prediction to string
remnantErrorModel to string
residualOptions to string
runOptions to string
MCMC reporting
violinPlot
Write prediction inputs
xtraModelInfo constructor.
An interface to the 'BaM' (Bayesian Modeling) engine, a 'Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of 'BaM' inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) <https://hal.science/hal-02606929v1>. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.