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How would you derive marginalization as it is given here:


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closed as not a real question by Did, William, Thomas, Ayman Hourieh, Noah Snyder Oct 4 '12 at 22:38

It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center.If this question can be reworded to fit the rules in the help center, please edit the question.

It is consensus that questions be self contained, so please write whatever it is that you're referencing in the page in your question's body. Thank you! – Pedro Tamaroff Jun 16 '12 at 2:49
The Wikipedia article you refer to has a good deal of detail, with some worked examples. If you have a concrete problem you are having difficulty with, perhaps you could give it explicitly. – André Nicolas Jun 16 '12 at 3:25
@PeterTamaroff The wikipedia page gives the PDF and says to marginalize you integrate. How would you derive marginalization starting from I assume Bayes Theorem? – Eiyrioü von Kauyf Jun 18 '12 at 18:40
A very good question. Perhaps you get better answers if you would state it as: "How to prove the Chapman–Kolmogorov equation?" (in its general form, not the variant limited to Markov chains) – Anne van Rossum Dec 21 '14 at 12:33
up vote 1 down vote accepted

Marginalizing amounts to integrating or summing out from joint distributions of several variables to the distribution of a smaller number of variables, usually to one variable. So for example if X and Y have the joint desnsity f(x,y) to get the maginal density for X you compute g(x)=∫f(x,y)dy integrated over all possible values of y. You do the analogous thing with sums for discrete variables.

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