When to use Linear Discriminant Analysis or Logistic Regression The Wikipedia article on Logistic Regression says:


  Logistic regression is an alternative to Fisher's 1936 method, linear
  discriminant analysis.  If the assumptions of linear discriminant
  analysis hold, application of Bayes' rule to reverse the conditioning
  results in the logistic model, so if linear discriminant assumptions
  are true, logistic regression assumptions must hold.  The converse is
  not true, so the logistic model has fewer assumptions than
  discriminant analysis and makes no assumption on the distribution of
  the independent variables.


Could someone help me to understand what the assumptions of linear discriminant are, an example of where they hold, and how application of Bayes' rule results in the Logistic Model.

Would it be correct to say that Logistic Regression is always the preferred choice?  Are there conditions where Linear Discriminant Analysis is to be preferred?
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