// 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
}
// Metropolis within Gibbs sampling algorithm of a posterior distribution.
func Gibbs(logpost func([]float64) float64, start []float64, m int, scale []float64) (vth [][]float64, arate []float64) {
// Arguments:
// logpost - function defining the log posterior density
// start - array with a single row that gives the starting value of the parameter vector
// m - the number of iterations of the chain
// scale - vector of scale parameters for the random walk Metropolis steps
// Returns:
// par - a matrix of simulated values where each row corresponds to a value of the vector parameter
// accept - vector of acceptance rates of the Metropolis steps of the algorithm
p := len(start)
//vth=array(0,dim=c(m,p))
// make vth matrix
nCol := p
s := make([]float64, m*nCol)
vth = make([][]float64, m)
for i, p := 0, 0; i < m; i++ {
vth[i] = s[p : p+nCol]
p += nCol
}
f0 := logpost(start)
//arate=array(0,dim=c(1,p))
// make arate vector
arate = make([]float64, p)
th0 := make([]float64, p)
for i, val := range start {
th0[i] = val
}
for i := 0; i < m; i++ {
for j := 0; j < p; j++ {
th1 := make([]float64, p)
for k, val := range th0 {
th1[k] = val
}
th1[j] = th0[j] + dst.NormalNext(0, 1)*scale[j]
f1 := logpost(th1)
// u=runif(1)<exp(f1-f0)
// th0[j]=th1[j]*(u==1)+th0[j]*(u==0)
// f0=f1*(u==1)+f0*(u==0)
if rand.Float64() < exp(f1-f0) {
th0[j] = th1[j]
f0 = f1
arate[j] += 1
}
vth[i][j] = th0[j]
}
}
for i, _ := range arate {
arate[i] /= float64(m)
}
return
}
// NormPostSim returns a simulated sample from the joint posterior distribution of the mean and variance for a normal
// sampling prior with a noninformative or informative prior. The prior assumes mu and sigma2 are
// independent with mu assigned a normal prior with mean mu0 and variance tau2, and sigma2 is
// assigned a inverse gamma prior with parameters a and b.
func NormPostSim(data []float64, a, b, mu0, tau2 float64, m int) (postMu, postS2 []float64) {
// Arguments:
// data - vector of observations
// prior params:
// a
// b
// mu0
// tau2
// 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)
sigma2 := s / float64(n)
for j := 0; j < m; j++ {
prec := float64(n)/sigma2 + 1/tau2
mu1 := (xbar*float64(n)/sigma2 + mu0/tau2) / prec
v1 := 1 / prec
// mu=rnorm(1,mu1,sqrt(v1))
mu := dst.NormalNext(mu1, sqrt(v1))
a1 := a + float64(n)/2
d2 := make([]float64, n)
for i, val := range data {
d2[i] = (val - mu) * (val - mu)
}
b1 := b + sum(d2)/2
sigma2 := rigamma(a1, b1)
postS2[j] = sigma2
postMu[j] = mu
}
return
}
// Test against R:moments
func TestSampleSkewness(t *testing.T) {
fmt.Println("Testing Skewness")
m := 10000000
mu := 0.0
sd := 1.0
d := make([]float64, m)
for i, _ := range d {
d[i] = dst.NormalNext(mu, sd)
}
x := Skew(d)
y := 0.0
if abs(x-y) > 1e-3 {
fmt.Println("failed: x, y ", x, y)
t.Error()
}
}
func TestSampleGeary(t *testing.T) {
fmt.Println("Testing Geary kurtosis")
m := 1000000
mu := 0.0
sd := 1.0
d := make([]float64, m)
for i, _ := range d {
d[i] = dst.NormalNext(mu, sd)
}
x := Geary(d)
y := 0.7979113
if abs(x-y) > 1e-2 {
fmt.Println("failed: x, y ", x, y)
t.Error()
}
}
func TestSampleMoments(t *testing.T) {
fmt.Println("Testing Moments")
m := 10000000
mu := 0.0
sd := 1.0
d := make([]float64, m)
for i, _ := range d {
d[i] = dst.NormalNext(mu, sd)
}
order := 4
central := false
absolute := false
x := moment(d, order, central, absolute)
y := 3.0
if abs(x-y) > 1e-2 {
fmt.Println("failed: x, y ", x, y)
t.Error()
}
}
// 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
}
// NormPostInfPriorNext returns a simulated tuple from the joint posterior distribution of the mean and variance for a normal
// sampling prior with a noninformative or informative prior. The prior assumes mu and sigma2 are
// independent with mu assigned a normal prior with mean mu0 and variance tau2, and sigma2 is
// assigned a inverse gamma prior with parameters a and b.
func NormPostInfPriorNext(data []float64, a, b, mu0, tau2 float64) (postMu, postS2 float64) {
// Arguments:
// data - vector of observations
// prior params:
// a
// b
// mu0
// tau2
// 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 / float64(n)
prec := float64(n)/postS2 + 1/tau2
mu1 := (xbar*float64(n)/postS2 + mu0/tau2) / prec
v1 := 1 / prec
postMu = dst.NormalNext(mu1, sqrt(v1))
a1 := a + float64(n)/2
d2 := make([]float64, n)
for i, val := range data {
d2[i] = (val - postMu) * (val - postMu)
}
b1 := b + sum(d2)/2
postS2 = rigamma(a1, b1)
return
}
// Test against R:LearningBayes:bfindep()
func TestFactCTableIndep(t *testing.T) {
y := [][]float64{
{1, 10},
{5, 20},
}
k := 20.0
m := 1000000
fmt.Println("Testing ldirichlet")
c5 := ldirichlet(y)
fmt.Println("ldirichlet: ", c5)
fmt.Println("should be: -2.302585 -12.266791")
fmt.Println("Testing meanSd")
nrm := make([]float64, m)
for i, _ := range nrm {
nrm[i] = dst.NormalNext(5, 0.6)
}
mu, sd := meanSd(nrm)
fmt.Println("mu, sd :", mu, sd)
fmt.Println("cols2vec")
fmt.Println(cols2vec(y))
fmt.Println("should be 1 5 10 20")
fmt.Println("Testing FactCTableIndep #1")
c1, c2 := FactCTableIndep(y, k, m)
c3, c4 := 0.6768298, 0.002508772
if !check(c1, c3) || !check(c2, c4) {
t.Error()
fmt.Println("FactCTableIndep: bf = ", c1, "should be ", c3, "+/- 0.015")
fmt.Println(" nse = ", c2, "should be ", c4, "+/- 0.003")
}
}