double BLSSS::log_model_prob(const Selector &g)const{
// borrowed from MLVS.cpp
double num = vpri_->logp(g);
if(num==BOOM::negative_infinity() || g.nvars() == 0) {
// If num == -infinity then it is in a zero support point in the
// prior. If g.nvars()==0 then all coefficients are zero
// because of the point mass. The only entries remaining in the
// likelihood are sums of squares of y[i] that are independent
// of g. They need to be omitted here because they are omitted
// in the non-empty case below.
return num;
}
SpdMatrix ivar = g.select(pri_->siginv());
num += .5*ivar.logdet();
if(num == BOOM::negative_infinity()) return num;
Vector mu = g.select(pri_->mu());
Vector ivar_mu = ivar * mu;
num -= .5*mu.dot(ivar_mu);
bool ok=true;
ivar += g.select(suf().xtx());
Matrix L = ivar.chol(ok);
if(!ok) return BOOM::negative_infinity();
double denom = sum(log(L.diag())); // = .5 log |ivar|
Vector S = g.select(suf().xty()) + ivar_mu;
Lsolve_inplace(L,S);
denom-= .5*S.normsq(); // S.normsq = beta_tilde ^T V_tilde beta_tilde
return num-denom;
}
int main(int argc, char **argv) {
SpdMatrix *pMat;
/*
* 1st test
*/
pMat = new SpdMatrix(4, 0);
pMat->setEntry(0, 0, 9);
pMat->setEntry(0, 1, 3);
pMat->setEntry(0, 2, -6);
pMat->setEntry(0, 3, 12);
pMat->setEntry(1, 1, 26);
pMat->setEntry(1, 2, -7);
pMat->setEntry(1, 3, -11);
pMat->setEntry(2, 2, 9);
pMat->setEntry(2, 3, 7);
pMat->setEntry(3, 3, 65);
cout << "Original matrix:" << endl;
pMat->print();
SquareMatrix l(pMat->chol());
// L matrix should be:
// 3 0 0 0
// 1 5 0 0
// -2 -1 2 0
// 4 -3 6 2
cout << "L matrix:" << endl;
l.print();
delete pMat;
}
Vector rmvn_ivar_mt(RNG &rng, const Vector &mu, const SpdMatrix &ivar) {
// Draws a multivariate normal with mean mu and precision matrix
// ivar.
bool ok = false;
Matrix U = ivar.chol(ok).transpose();
if (!ok) {
report_error("Cholesky decomposition failed in rmvn_ivar_mt.");
}
return rmvn_precision_upper_cholesky_mt(rng, mu, U);
}
Matrix chol(const SpdMatrix &S, bool & ok){return S.chol(ok);}
Matrix chol(const SpdMatrix &S){ return S.chol();}
Vector rmvn_mt(RNG &rng, const Vector &mu, const SpdMatrix &V) {
bool okay = true;
Matrix L = V.chol(okay);
if (okay) return rmvn_L_mt(rng, mu, L);
return rmvn_robust_mt(rng, mu, V);
}