//---------------------------------------------------------------------------
SpdMatrix Marginal::forecast_precision() const {
const Selector &observed(model_->observed_status(time_index()));
DiagonalMatrix observation_precision =
model_->observation_variance(time_index()).inv();
const SparseKalmanMatrix *observation_coefficients(
model_->observation_coefficients(time_index(), observed));
SpdMatrix variance;
if (previous()) {
variance = previous()->state_variance();
} else {
variance = model_->initial_state_variance();
}
// 'inner' is I + P * Z' Hinv Z
Matrix inner = variance * observation_coefficients->inner(
observation_precision.diag());
inner.diag() += 1.0;
SpdMatrix outer = inner.solve(variance);
SpdMatrix ans = observation_precision.sandwich(
observation_coefficients->sandwich(outer));
ans *= -1;
ans.diag() += observation_precision.diag();
return ans;
}
//---------------------------------------------------------------------------
SpdMatrix Marginal::direct_forecast_precision() const {
SpdMatrix variance;
if (previous()) {
variance = previous()->state_variance();
} else {
variance = model_->initial_state_variance();
}
const Selector &observed(model_->observed_status(time_index()));
SpdMatrix ans = model_->observation_coefficients(
time_index(), observed)->sandwich(variance);
ans.diag() += model_->observation_variance(time_index()).diag();
return ans.inv();
}