@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr
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@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr
## Standard Least Squares (SLS) Bayesian inference
## 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.
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.
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.
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.
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.
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.
