//----------------------------------------------------------------------
void ProbitBartPosteriorSampler::impute_latent_data_point(DataType *data) {
double eta = data->prediction();
int n = data->n();
int number_positive = data->y();
int number_negative = n - number_positive;
double sum_of_probits = 0;
if (number_positive > 5) {
double mean = 0;
double variance = 1;
trun_norm_moments(eta, 1, 0, true, &mean, &variance);
sum_of_probits += rnorm_mt(rng(),
number_positive * mean,
sqrt(number_positive * variance));
} else {
for (int i = 0; i < number_positive; ++i) {
sum_of_probits += rtrun_norm_mt(rng(), eta, 1, 0, true);
}
}
if (number_negative > 5) {
double mean = 0;
double variance = 1;
trun_norm_moments(eta, 1, 0, false, &mean, &variance);
sum_of_probits += rnorm_mt(rng(),
number_negative * mean,
sqrt(number_negative * variance));
} else {
for (int i = 0; i < number_negative; ++i) {
sum_of_probits += rtrun_norm_mt(rng(), eta, 1, 0, false);
}
}
data->set_sum_of_residuals(sum_of_probits - (n * eta));
}
double BinomialProbitDataImputer::impute(RNG &rng, double number_of_trials,
double number_of_successes,
double eta) const {
int64_t n = lround(number_of_trials);
int64_t y = lround(number_of_successes);
if (y < 0 || n < 0) {
report_error(
"Negative values not allowed in "
"BinomialProbitDataImputer::impute().");
}
if (y > n) {
report_error(
"Success count exceeds trial count in "
"BinomialProbitDataImputer::impute.");
}
double mean, variance;
double ans = 0;
if (y > clt_threshold_) {
trun_norm_moments(eta, 1, 0, true, &mean, &variance);
// If we draw y deviates from the same truncated normal and add
// them up we'll have a normal with mean (y * mean) and variance
// (y * variance).
ans += rnorm_mt(rng, y * mean, sqrt(y * variance));
} else {
for (int i = 0; i < y; ++i) {
// TODO: If y is large-ish but not quite
// clt_threshold_ then we might waste some time here
// constantly rebuilding the same TnSampler object.
ans += rtrun_norm_mt(rng, eta, 1, 0, true);
}
}
if (n - y > clt_threshold_) {
trun_norm_moments(eta, 1, 0, false, &mean, &variance);
ans += rnorm_mt(rng, (n - y) * mean, sqrt((n - y) * variance));
} else {
for (int i = 0; i < n - y; ++i) {
ans += rtrun_norm_mt(rng, eta, 1, 0, false);
}
}
return ans;
}
