Hi,
How would you make the variance of a LKJCholeskyCov
depend on some other regressor values ?
with model:
packed_L = pm.LKJCholeskyCov('packed_L', n=shape, eta=2, sd_dist=pm.HalfCauchy.dist(2.5))
L = pm.expand_packed_triangular(shape, packed_L)
Σ = pm.Deterministic('Σ', L.dot(L.T))
mu_1= mu_variable(f_data, 'mu_1') # returns a linear combination of regressor beta * regressor value
mu_2 = mu_variable(f_data, 'mu_2')
mu = T.stack([eps_mu, pe_mu]).T
obs = pm.MvNormal('obs', mu=mu, chol=L, observed=target)
trace = pm.sample(3000, cores=1)
In this model, mu
depends on inputs, I would like to make sd_dist
depend on inputs as well. How could I do that?
Many thanks,
Maxime.