AARE Symp Paper. Nov.


There are some excellent accounts of making use of Bayes nets. Pennies starting to drop how these things can learn as you add more and more data. I found this tutorial from Norsys useful.


OK. I've come to understand that so much of ML is based upon Bayesian stats. So back to class. I began with Kevin Binz's excellent intro to Bayes theorem. He points to a short film from the Khan academy by Brit Cruise which does the intro to trees really well. Then via YouTube's suggested related clips I came to Richard Carrier's excellent and much longer clip in which he points to the following books:

McGrayne, S. B. (2011). The theory that would not die: how Bayes' rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy. New Haven Conn.: Yale University Press.

This book is a gem, McGrayne's.

Paulos, J. A. (2001). Innumeracy : mathematical illiteracy and its consequences. New York: Hill and Wang.


Seife, C. (2010). Proofiness : the dark arts of mathematical deception. New York: Viking.


I've been collecting material, reading and doing some scribbling offline and this AM came across a piece by @DaveWiner which nudged my thinking back to this way of working.

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