## 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[θ|x] using linear interpolation to approximate the density function and numerical integration where θ 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)
```