Multivariate LogNormal distribution

Questions v5
7

We just released pymc 5.2.0, which can infer logp of transformed multivariate distributions.

I am able to run the following snippet:

import pymc as pm
import numpy as np
import arviz as az

def mvlognormal_dist(mu, cov, size):
    return pm.math.exp(pm.MvNormal.dist(mu, cov, size=size))

class MvLogNormal:
    def __new__(cls, name, mu, cov, **kwargs): 
        return pm.CustomDist(name, mu, cov, dist=mvlognormal_dist, ndim_supp=1, **kwargs)
    
    @classmethod
    def dist(cls, mu, cov, **kwargs):
        return pm.CustomDist.dist(mu, cov, class_name="MvLogNormal", dist=mvlognormal_dist, ndim_supp=1, **kwargs)
    
with pm.Model() as m:
    mu = pm.Normal("mu", shape=(3,))
    x = MvLogNormal("x", mu=mu, cov=np.eye(3)*1e-3, observed=np.exp([1, 2, 3]))
    trace = pm.sample()

az.summary(trace)

        mean     sd  hdi_3%  hdi_97%  ...  mcse_sd  ess_bulk  ess_tail  r_hat
mu[0]  0.999  0.031   0.940    1.055  ...      0.0    7103.0    3448.0    1.0
mu[1]  1.999  0.032   1.935    2.054  ...      0.0    6510.0    3054.0    1.0
mu[2]  2.997  0.032   2.937    3.055  ...      0.0    5575.0    2972.0    1.0