# Bayesian correlation coefficient

Hi, I was attempting to infer Pearson-like correlation coefficients using this PYMC3-based notebook as basis, but I cannot figure out how to initiate the precision matrix for PYMC5. The core code is this:

``````import theano.tensor as T

def precision(sigma, rho):
C = T.alloc(rho, 2, 2)
C = T.fill_diagonal(C, 1.)
S = T.diag(sigma)
return T.nlinalg.matrix_inverse(S.dot(C).dot(S))

def analyze(data):
with pm.Model() as model:
# priors might be adapted here to be less flat
mu = pm.Normal('mu', mu=0., tau=0.000001, shape=2, testval=np.mean(data, axis=1))
sigma = pm.Uniform('sigma', lower=1e-6, upper=1000., shape=2, testval=np.std(data, axis=1))
rho = pm.Uniform('r', lower=-1., upper=1., testval=0)

prec = pm.Deterministic('prec', precision(sigma, rho))
mult_n = pm.MvNormal('mult_n', mu=mu, tau=prec, observed=data.T)

return model
``````

I will try to figure out how in the meantime and might answer myself, I think it is the type of functionality someone else might find this useful.

The advice is usually to use a LKJCholeskyCov as a prior, because every other form of covariance prior tends to sample poorly.

There are a couple of examples in the gallery that use it.

This one is still in PyMC v4 but should be similar with the latest release: LKJ Cholesky Covariance Priors for Multivariate Normal Models — PyMC example gallery

1 Like

Thank so much for referring me to that link, I was unaware of this. Thanks @ricardoV94 ! Marking as solved as well