title: Logistic Regression with pymc3 - what's the prior for build in glm?
body: <p>I could not find good explanation for what's going on exactly by using glm with pymc3 in case of logistic regression. So I  compared the GLM version to an explicit pymc3 model. I started to write an ipython notebook for documentation, see:</p>
  
  <p><a href="http://christianherta.de/lehre/dataScience/machineLearning/mcmc/logisticRegressionPymc3.slides.php" rel="nofollow">http://christianherta.de/lehre/dataScience/machineLearning/mcmc/logisticRegressionPymc3.slides.php</a> </p>
  
  <p>What I don't understand is:</p>
  
  <ul>
  <li><p>What prior is used for the Parameters in GLM? I assume they are also Normal distributed. I got different results with my explicit model in comparison to the build in GLM. (see link above)</p></li>
  <li><p>With less data the sampling get's stuck and/or I got really poor results. With more training data I could not observe this behaviour. Is this normal for mcmc? </p></li>
  </ul>
  
  <p>There are more issue in the notebook.</p>
  
  <p>Thanks for your answer.</p>