Function lama 

pub fn lama(
    h: &ArrayView2<'_, C64>,
    y: &[C64],
    noise_var: f64,
    config: &MessagePassing,
    variance: AmpVariance,
) -> Result<Trace>

LAMA (Jeon et al., Algorithm 2) on $y = H x + w$, $w$ of variance noise_var per entry, with the prior, domain and iterations of config and the variance estimate variance: the columns of $H$ are first scaled to a unit mean norm (with $y$ and the noise variance), which leaves the problem unchanged.

Errors

comnum_core::Error::Shape for an empty channel or one whose rows do not match $y$; comnum_core::Error::Config for a negative noise variance, zero iterations or a zero channel.