Description Usage Arguments Value Author(s) Examples
Return n equally weighted posterior samples
1 | getEqualSamples(posterior, n = Inf)
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posterior |
Matrix from the output of nested sampling. Should have log weights in column 1, log likelihoods in column 2 and then the parameter values in the remaining column(s). |
n |
Number of samples from the posterior required. If infinity (the deafult) this will return the maximum number of equally weighted samples generated from the posterior it can. Likewise if n is greater than this maximum number of samples. |
A set of equally weighted samples from the inferred posterior distribution.
Lydia Rickett, Matthew Hartley, Richard Morris and Nick Pullen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | mu <- 1
sigma <- 1
data <- rnorm(100, mu, sigma)
transform <- function(params) {
tParams = numeric(length=length(params))
tParams[1] = GaussianPrior(params[1], mu, sigma)
tParams[2] = UniformPrior(params[2], 0, 2 * sigma)
return(tParams)
}
llf <- function(params) {
tParams = transform(params)
mean = tParams[1]
sigma = tParams[2]
n <- length(data)
ll <- -(n/2) * log(2*pi) - (n/2) * log(sigma**2) - (1/(2*sigma**2)) * sum((data-mean)**2)
return(ll)
}
prior.size <- 25
tol <- 0.5
ns.results <- nestedSampling(llf, 2, prior.size, transform, tolerance=tol)
getEqualSamples(ns.results$posterior)
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