## Posterior standard deviation

### Description

Posterior standard deviation

### Usage

## S3 method for class 'Bolstad'
sd(x, ...)


### Arguments

 x an object of class Bolstad for which we want to compute the standard deviation. ... Any additional arguments to be passed to sd. Calculate the posterior standard deviation of an object of class Bolstad. If the object has a member sd then it will return this value otherwise it will calculate the posterior standard deviation sd[\theta|x] using linear interpolation to approximate the density function and numerical integration where \theta is the variable for which we want to do Bayesian inference, and x is the data.

James M. Curran

### Examples

## The usefulness of this method is really highlighted when we have a general
## continuous prior. In this example we are interested in the posterior
## standard deviation of an normal mean. Our prior is triangular over [-3, 3]
set.seed(123)
x = rnorm(20, -0.5, 1)

mu = seq(-3, 3, by = 0.001)

mu.prior = rep(0, length(mu))
mu.prior[mu <= 0] = 1 / 3 + mu[mu <= 0] / 9
mu.prior[mu > 0] = 1 / 3 - mu[mu > 0] / 9

results = normgcp(x, 1, density = "user", mu = mu, mu.prior = mu.prior, plot = FALSE)
sd(results)