title: Numpy covariance matrix nonrmalisation
body: <p>I know that <code>numpy.cov</code> calculates the covariance given a N dimensional array. </p>
  
  <p>I can see <a href="https://github.com/numpy/numpy/blob/v1.9.1/numpy/lib/function_base.py#L1780" rel="nofollow noreferrer">from the documentation on GitHub</a> that the normalisation is done by <code>(N-1)</code>. But for my specific case, the covariance matrix is given by:</p>
  
  <p><img src="https://i.stack.imgur.com/kHUIT.png" alt="enter image description here"></p>
  
  <p>where <code>xi</code>  is the quantity. <code>i</code> and <code>j</code> are the bins.</p>
  
  <p>As you can see from the above equation, this covariance matrix is normalised by <code>(N-1)/N</code>. </p>
  
  <p><strong>TO GET THE ABOVE NORMALISATION</strong></p>
  
  <p><strong><em>Can I simply multiply the covariance matrix obtained from <code>numpy.cov</code> by <code>(N-1)**2 / N</code> to get the above normalisation? Is that correct?</em></strong></p>
  
  <p><strong><em>Or Should I use the <code>bias</code> parameter inside <code>numpy.cov</code>? If so how?</em></strong> </p>