T Selector::sub_select(const T &x, const Selector &rhs) const {
assert(rhs.nvars() <= this->nvars());
assert(this->covers(rhs));
Selector tmp(nvars(), false);
for (uint i = 0; i < rhs.nvars(); ++i) {
tmp.add(INDX(rhs.indx(i)));
}
return tmp.select(x);
}
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;
}
void BLSSS::draw_beta() {
Selector g = m_->coef().inc();
if(g.nvars() == 0) {
m_->drop_all();
return;
}
SpdMatrix ivar = g.select(pri_->siginv());
Vector ivar_mu = ivar * g.select(pri_->mu());
ivar += g.select(suf().xtx());
ivar_mu += g.select(suf().xty());
Vector b = ivar.solve(ivar_mu);
b = rmvn_ivar_mt(rng(), b, ivar);
// If model selection is turned off and some elements of beta
// happen to be zero (because, e.g., of a failed MH step) we don't
// want the dimension of beta to change.
m_->set_included_coefficients(b, g);
}
void BLSSS::draw_beta() {
Selector g = model_->coef().inc();
if (g.nvars() == 0) {
model_->drop_all();
return;
}
SpdMatrix precision = g.select(slab_->siginv());
Vector scaled_mean = precision * g.select(slab_->mu());
precision += g.select(suf().xtx());
Cholesky precision_cholesky_factor(precision);
scaled_mean += g.select(suf().xty());
Vector posterior_mean = precision_cholesky_factor.solve(scaled_mean);
Vector beta = rmvn_precision_upper_cholesky_mt(
rng(), posterior_mean, precision_cholesky_factor.getLT());
// If model selection is turned off and some elements of beta
// happen to be zero (because, e.g., of a failed MH step) we don't
// want the dimension of beta to change.
model_->set_included_coefficients(beta, g);
}
