The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
There is no built-in way to define hard limits for the parameter and make sure they never go outside of this range.
The recommended way to address this issue is to handle these cases in
the function f you provide.
For example, to keep parameters in the 0-1 range:
p.log.restricted <- function(x) {
if (any(x < 0, x > 1)) {
return(-Inf)
}
B <- 0.03 # controls 'bananacity'
-x[1]^2 / 200 - 1 / 2 * (x[2] + B * x[1]^2 - 100 * B)^2
}
res <- simpleMH(
p.log.restricted,
inits = c(a = 0, b = 0),
theta.cov = diag(2),
max.iter = 3000,
coda = TRUE
)
summary(res$samples)
#>
#> Iterations = 1:3000
#> Thinning interval = 1
#> Number of chains = 1
#> Sample size per chain = 3000
#>
#> 1. Empirical mean and standard deviation for each variable,
#> plus standard error of the mean:
#>
#> Mean SD Naive SE Time-series SE
#> a 0.4641 0.2908 0.005308 0.02478
#> b 0.6694 0.2567 0.004687 0.02460
#>
#> 2. Quantiles for each variable:
#>
#> 2.5% 25% 50% 75% 97.5%
#> a 0.03361 0.1939 0.4530 0.7041 0.9794
#> b 0.12468 0.4794 0.7622 0.8668 0.9938These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.