pypcga.PCGA.get_dense_post_cov#

PCGA.get_dense_post_cov(is_direct_solve: bool | None = None, inflation: float | None = None) → ndarray[tuple[Any, ...], dtype[float64]][source]#

Return the dense posterior covariance matrix..

Notes

This is not practical for large scale models.

Parameters:

is_direct_solve (Optional[bool], optional) – _description_, by default None
inflation (Optional[float], optional) – Inflation factor used to build the posterior covariance matrix. If None, the random_state used by PCGA is taken. By default None.

Notes

This is practical for posterior sampling.