// NormPostSimNoPrior returns a simulated sample from the joint posterior distribution of the mean and variance for a normal
// sampling prior.
func NormPostSimNoPrior(data []float64, m int) (postMu, postS2 []float64) {
// Arguments:
// data - vector of observations
// m - number of simulations desired
// Returns:
// mu - vector of simulated draws of normal mean
// sigma2 - vector of simulated draws of normal variance
xbar := mean(data)
n := len(data)
diff2 := make([]float64, n)
for i, val := range data {
diff2[i] = (val - xbar) * (val - xbar)
}
s := sum(diff2)
postS2 = make([]float64, m)
postMu = make([]float64, m)
for i, _ := range postMu {
postS2[i] = s / dst.ChiSquareNext(int64(n)-1)
sd := sqrt(postS2[i]) / sqrt(float64(n))
postMu[i] = dst.NormalNext(xbar, sd)
}
return
}
// NormPostNoPriorNext returns a sampled tuple from the joint posterior distribution of the mean and variance for a normal
// sampling prior.
func NormPostNoPriorNext(data []float64) (postMu, postS2 float64) {
// Arguments:
// data - vector of observations
// Returns:
// postMu - simulated draw of normal mean
// postS2 - simulated draw of normal variance
xbar := mean(data)
n := len(data)
diff2 := make([]float64, n)
for i, val := range data {
diff2[i] = (val - xbar) * (val - xbar)
}
s := sum(diff2)
postS2 = s / dst.ChiSquareNext(int64(n)-1)
sd := sqrt(postS2) / sqrt(float64(n))
postMu = dst.NormalNext(xbar, sd)
return
}