Introduction to Bayesian Methods

I find it prohibitively difficult to extract information from out-of-context slides. Especially if the slides are supposed to be an introduction to a new topic.

As a rather short (9 pages) introduction to Bayesian inference, I remember greatly appreciating an extract from the book "Information Theory, Inference, and Learning Algorithms" by David MacKay.<p>The relevant part is on PDF pages 469 to 477 here: &#60;<a href="http://www.inference.phy.cam.ac.uk/itprnn/book.pdf&#62" rel="nofollow">http://www.inference.phy.cam.ac.uk/itprnn/book.pdf&#62</a>;<p>It explains how Bayesian inference works (which is intuitive and not strange at all) and gives examples of absurdities that comes upon you when you don't do it this way.<p>Highlights:<p>• "Let me through, I’m a Bayesian" (When analysing the effectivness of a vaccine)<p>• "I have no problem with the idea that there is only one answer to a well-posed problem" (In response to sampling theorists wide selection of ad hoc procedures)

Pet peeve: Conditioning on a non-random variable doesn't make sense (i.e. P(data | H_0))[1].<p>[1] <a href="http://normaldeviate.wordpress.com/2013/03/14/double-misunderstandings-about-p-values/" rel="nofollow">http://normaldeviate.wordpress.com/2013/03/14/double-misunde...</a>

Direct link to the slides in PDF: <a href="http://madere.biol.mcgill.ca/cchivers/BIOL597_bayesian.pdf" rel="nofollow">http://madere.biol.mcgill.ca/cchivers/BIOL597_bayesian.pdf</a>

Is anyone out there in SF and wants to give a presentation on something in this area and its applications for the SFHN Speaker Series?

you are not invited to the big data party