getEqualSamples {babar} R Documentation

## getEqualSamples

### Description

Return n equally weighted posterior samples

### Usage

`getEqualSamples(posterior, n = Inf)`

### Arguments

 `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.

### Value

A set of equally weighted samples from the inferred posterior distribution.

### Author(s)

Lydia Rickett, Matthew Hartley, Richard Morris and Nick Pullen

### Examples

```
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)
```

[Package babar version 1.0 Index]