@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr
## Standard Least Squares (SLS) Bayesian inference
We show how to use the DRAM algorithm for SLS Bayesian inference, with the `modMCMC()` function of the [FME](https://cran.r-project.org/web/packages/FME/) package.
We show how to use the DRAM algorithm for SLS Bayesian inference, with the `modMCMC()` function of the [FME](https://cran.r-project.org/package=FME) package.
First, we need to define a function that returns twice the opposite of the log-likelihood for a given parameter set.
Nota: in the `RunAirGR4J()` function, the computation of the log-likelihood is simplified in order to ensure a good computing performance. It corresponds to a translation of the two following lines.
There are several diagnostics that can be used to check the convergence of the chains.
The R package [coda](https://cran.r-project.org/web/packages/coda/index.html) provides several diagnostic tests.
The R package [coda](https://cran.r-project.org/package=coda) provides several diagnostic tests.
Among others, the Gelman and Rubin's convergence can be used. A value close to 1 suggests acceptable convergence.
The result will be better with more iterations than 2000. As we kept the iterations during the convergence process, we have to set the `autoburnin` argument to `TRUE` in order to consider only the second half of the series.