Nothing
cost_gaussian = function (x, data_x, data_y, prior_p, prior_st)
{
data_num = dim(data_x)[1]
dm = dim(data_x)[2]
hn = data_num^(-1/(4 + dm))
tau2 = h = vector(, dm)
for (k in 1:dm) {
tau2[k] = exp(x[k])
h[k] = sqrt(tau2[k]) * hn
}
hprod = prod(h)
cont = exp(-0.5 * dm * log(2 * pi))
cv_int = vector(,data_num)
for(i in 1:data_num)
{
temp = (sweep(data_x[-i,], 2, data_x[i,])/h)^2
weight = cont * exp(-0.5 * apply(temp,1,sum))/hprod
suma = sum(weight * data_y[-i])
sumb = sum(weight)
cv_int[i] = (data_y[i] - suma/sumb)^2
}
cv = sum(cv_int)
logp = -0.5 * (data_num + prior_p) * log(0.5 * cv + 0.5 * prior_st)
for (i in 1:dm) {
logp = logp + x[i] + logpriorh2(tau2[i] * hn * hn) + log(hn * hn)
}
return(-logp)
}
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