oilmm_mll#
- gpjax.models.oilmm_mll(model, data)[source]#
Log marginal likelihood for the OILMM.
Implements Prop. 9 from Bruinsma et al. (2020):
log p(Y) = correction_terms + sum_i log N((TY)_i | 0, K_i + noise_i I_n)
The correction terms prevent the projection from collapsing and account for data in the (p - m) dimensions orthogonal to the mixing matrix.
- Parameters:
model (OILMMModel) – OILMMModel with parameters to evaluate.
data (Dataset) – Training data with X [N, D] and y [N, P].
- Returns:
Scalar log marginal likelihood.
- Return type: