Fit a univariate normal (Gaussian) distribution to the observed data.

Fit a univariate normal (Gaussian) distribution to the observed data.

gibbsNorm(y, niter = 1000, priors = NULL)

Arguments

y

A vector of observed pixel data.
niter

The number of iterations of the algorithm to perform.
priors

A list of priors for the parameters of the model.

Value

A list containing MCMC samples for the mean and standard deviation.

Examples

y <- rnorm(100,mean=5,sd=2)
res.norm <- gibbsNorm(y, priors=list(mu=0, mu.sd=1e6, sigma=1e-3, sigma.nu=1e-3))
summary(res.norm$mu[501:1000])
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   4.548   5.015   5.158   5.154   5.289   5.794 
summary(res.norm$sigma[501:1000])
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.778   2.013   2.117   2.121   2.208   2.808