title: PyMC3 passing stochastic covariance matrix to pm.MvNormal()
body: <p>I've tried to fit a simple 2D gaussian model to observed data by using PyMC3.</p>
  
  <pre><code>import numpy as np
  import pymc3 as pm
  
  n = 10000;
  np.random.seed(0)
  X = np.random.multivariate_normal([0,0], [[1,0],[0,1]], n);
  
  with pm.Model() as model:
      # PRIORS
      mu = [pm.Uniform('mux', lower=-1, upper=1), 
            pm.Uniform('muy', lower=-1, upper=1)]
      cov = np.array([[pm.Uniform('a11', lower=0.1, upper=2), 0],
                      [0, pm.Uniform('a22', lower=0.1, upper=2)]])
  
      # LIKELIHOOD
      likelihood = pm.MvNormal('likelihood', mu=mu, cov=cov, observed=X)
  
  with model:
      trace = pm.sample(draws=1000, chains=2, tune=1000)
  </code></pre>
  
  <p>while I can do this in 1D by passing the <code>sd</code> to <code>pm.Normal</code>, I have some trouble in passing the covariance matrix to <code>pm.MvNormal</code>.</p>
  
  <p>Where am I going wrong?</p>