Define the class for the posterior parameter of SuShiE model.

Variables:

alpha : jax.Array

Posterior probability for SNP to be causal (i.e., \(\alpha\) in Model Description; \(L \times p\)).

post_mean : jax.Array

The alpha-weighted posterior mean for each SNP (\(L \times p \times k\)).

post_mean_sq : jax.Array

The alpha-weighted posterior mean square for each SNP (\(L \times p \times k \times k\) , a diagonal matrix for \(k \times k\)).

weighted_sum_covar : jax.Array

The alpha-weighted sum of posterior effect covariance across SNPs (\(L \times k \times k\)).

kl : jax.Array

The Kullback–Leibler (KL) divergence for each \(L\).

Infer Public-members¶

static Posterior(_cls, alpha: Array, post_mean: Array, ...)

Create new instance of Posterior(alpha, post_mean, post_mean_sq, weighted_sum_covar, kl)

__repr__()

Return a nicely formatted representation string

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.