dot-core_reconc_TDcond: Core Reconciliation via Top-Down Conditioning for Mixed...

In bayesRecon: Probabilistic Reconciliation via Conditioning

Core Reconciliation via Top-Down Conditioning for Mixed Hierarchies

Description

Internal function that performs the core reconciliation logic using top-down conditioning (TD) for mixed hierarchies. First, upper forecasts are reconciled analytically via conditioning (if necessary), then bottom distributions are updated through probabilistic top-down procedure by conditioning on the reconciled upper values.

Usage

.core_reconc_TDcond(
  A,
  mean_upper,
  cov_upper,
  L_pmf,
  num_samples,
  return_type,
  return_upper = TRUE,
  suppress_warnings = TRUE,
  min_fraction_samples_ok = .MIN_FRACTION_SAMPLES_OK
)

Arguments

A	Matrix (n_upper x n_bottom) defining the hierarchy where upper = A %*% bottom.
mean_upper	Vector of mean forecasts for upper level.
cov_upper	Covariance matrix of upper level forecasts.
L_pmf	List of PMF objects representing the bottom level base forecasts.
num_samples	Number of samples to draw from the reconciled distribution.
return_type	Character string specifying return format: 'pmf', 'samples', or 'all'.
return_upper	Logical, whether to return reconciled upper forecasts (default TRUE).
suppress_warnings	Logical, whether to suppress warnings about samples outside support (default TRUE).
min_fraction_samples_ok	Numeric between 0 and 1, minimum fraction of reconciled upper samples that must lie in the support of the bottom-up distribution (default 0.5). If the fraction is below this threshold, the function returns an error.

Details

The function internally:

1. Identifies the "lowest upper" nodes in the hierarchy.

2. If all uppers are lowest-uppers, samples directly from the upper MVN. Otherwise, analytically reconciles the upper hierarchy and samples from the lowest level.

3. Reconciles bottom distributions by conditioning on the sampled/reconciled upper values using the probabilistic top-down algorithm.

4. Discards samples that fall outside the support of the bottom-up distribution.

Value

A list containing:

• bottom_rec_pmf: list of PMF objects for each bottom series (only if return_type is 'pmf' or 'all').

• bottom_rec_samples: matrix (n_bottom x num_samples) of reconciled bottom samples (only if return_type is 'samples' or 'all').

• upper_rec_pmf: list of PMF objects for each upper series (only if return_type is 'pmf' or 'all', and return_upper = TRUE).

• upper_rec_samples: matrix (n_upper x num_samples) of reconciled upper samples (only if return_type is 'samples' or 'all', and return_upper = TRUE).

bayesRecon documentation built on March 8, 2026, 9:08 a.m.