title: Maximum Likelihood Estimate pseudocode
body: <p>I need to code a Maximum Likelihood Estimator to estimate the mean and variance of some toy data.  I have a vector with 100 samples, created with <code>numpy.random.randn(100)</code>. The data should have zero mean and unit variance Gaussian distribution.</p>
  
  <p>I checked Wikipedia and some extra sources, but I am a little bit confused since I don't have a statistics background.</p>
  
  <p>Is there any pseudo code for  a maximum likelihood estimator? I get the intuition of MLE but I cannot figure out where to start coding.</p>
  
  <p>Wiki says taking argmax of log-likelihood. What I understand is: I need to calculate log-likelihood by using different parameters and then I'll take the parameters which gave the maximum probability. What I don't get is: where will I find the parameters in the first place? If I randomly try different mean &amp; variance to get a high probability, when should I stop trying?</p>