title: How can I sample a multivariate log-normal distribution in Python?
body: <p>Using Python, how can I sample data from a multivariate log-normal distribution? For instance, for a multivariate normal, there are two options. Let's assume we have a 3 x 3 covariance matrix and a 3-dimensional mean vector mu.</p>
  
  <pre><code># Method 1
  sample = np.random.multivariate_normal(mu, covariance)
  
  # Method 2
  L = np.linalg.cholesky(covariance)
  sample = L.dot(np.random.randn(3)) + mu
  </code></pre>
  
  <p>I found numpy's <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.lognormal.html" rel="noreferrer">numpy.random.lognormal</a>, but that only seems to work for univariate samples. I also noticed scipy's <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html#scipy.stats.lognorm.rvs" rel="noreferrer">scipy.stats.lognorm</a>. This does seem to have the potential for multivariate samples. However, I can't figure out how to do this.</p>