## Generate a test likelihood function. ll <- generateTestDensityMultiNormal(sigma = "no correlation") ## Create a BayesianSetup object from the likelihood ## is the recommended way of using the runMCMC() function. bayesianSetup <- createBayesianSetup(likelihood = ll, lower = rep(-10, 3), upper = rep(10, 3)) ## Finally we can run the sampler and have a look settings = list(iterations = 1000, adapt = FALSE) out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "Metropolis", settings = settings) ## out is of class bayesianOutput. There are various standard functions # implemented for this output plot(out) correlationPlot(out) marginalPlot(out) summary(out) ## additionally, you can return the sample as a coda object, and make use of the coda functions # for plotting and analysis codaObject = getSample(out, start = 500, coda = TRUE)
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