double BLSSS::find_posterior_mode() {
BinomialLogitUnNormalizedLogPosterior logpost(m_, pri_.get());
const Selector &inc(m_->coef().inc());
Vector beta(m_->included_coefficients());
int dim = beta.size();
if (dim == 0) {
return negative_infinity();
// TODO: This logic prohibits an empty model. Better to return
// the actual value of the un-normalized posterior, which in
// this case would just be the likelihood portion.
} else {
Vector gradient(dim);
Matrix hessian(dim, dim);
double logf;
std::string error_message;
bool ok = max_nd2_careful(beta,
gradient,
hessian,
logf,
Target(logpost),
dTarget(logpost),
d2Target(logpost),
1e-5,
error_message);
if (ok) {
m_->set_included_coefficients(beta, inc);
return logf;
} else {
return negative_infinity();
}
}
}
void LogitEMC::find_posterior_mode(){
if(!pri_){
ostringstream err;
err << "Logit_EMC cannot find posterior mode. "
<< "No prior is set." << endl;
throw_exception<std::runtime_error>(err.str());
}
d2LoglikeTF loglike(this);
d2LogPostTF logpost(loglike, pri_);
Vec b = this->beta();
uint dim = b.size();
Vec g(dim);
Mat h(dim,dim);
b = max_nd2(b, g, h, Target(logpost), dTarget(logpost),
d2Target(logpost), 1e-5);
this->set_beta(b);
}
void LogitEMC::find_posterior_mode(){
if(!pri_){
ostringstream err;
err << "Logit_EMC cannot find posterior mode. "
<< "No prior is set." << endl;
report_error(err.str());
}
d2LoglikeTF loglike(this);
d2LogPostTF logpost(loglike, pri_);
Vector b = this->Beta();
uint dim = b.size();
Vector g(dim);
Matrix h(dim,dim);
b = max_nd2(b, g, h, Target(logpost), dTarget(logpost),
d2Target(logpost), 1e-5);
this->set_Beta(b);
}
double PRSS::find_posterior_mode() {
const Selector &included(model_->inc());
d2TargetFunPointerAdapter logpost(
boost::bind(&PoissonRegressionModel::log_likelihood, model_,
_1, _2, _3, _4),
boost::bind(&MvnBase::logp_given_inclusion, slab_prior_.get(),
_1, _2, _3, included, _4));
Vector beta = model_->included_coefficients();
int dim = beta.size();
if (dim == 0) {
return negative_infinity();
// TODO: This logic prohibits an empty model. Better to return
// the actual value of the un-normalized log posterior, which in
// this case would just be the likelihood portion.
}
Vector gradient(dim);
Spd hessian(dim);
double logf;
std::string error_message;
bool ok = max_nd2_careful(beta,
gradient,
hessian,
logf,
Target(logpost),
dTarget(logpost),
d2Target(logpost),
1e-5,
error_message);
if (ok) {
model_->set_included_coefficients(beta, included);
return logf;
} else {
return negative_infinity();
}
}