logPrior_Flat

Computes the log-density of an improper flat prior distribution.

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>.

Benjamin Renard

RBaM

Bayesian Modeling: Estimate a Computer Model and Make Uncertain
Predictions

INRAE 

Ministère de la Transition Ecologique - SCHAPI 

logPrior_Flat function

<dl><dt>parvector</dt>
<dd>numeric vector, parameter vector, including thetas (model parameters)
and gammas (structural errors parameters).</dd></dl>

Arguments

Log-prior function: improper flat prior — logPrior_Flat

<dl>

<dt>parvector</dt>
<dd>numeric vector, parameter vector, including thetas (model parameters)
and gammas (structural errors parameters).</dd>

</dl>

logPrior_Flat: Log-prior function: improper flat prior

Description

Usage

Value

Arguments

Examples