24 Nov 2004 graydon   » (Master)

free runtimes summit

Red Hat had a little summit which I attended last week, showing off the excellent work our free java hackers have been up to lately. But it was not all show and tell; an important theme to this meeting was getting various disagreeing people to talk face to face, with civility, rather than fighting through email.

Personally I don't like fighting much anymore. I'm particularly uninterested in the java and C# fight. So I wrote up a little exploration of the differences, to see if we can't just learn to live with them as minor dialects of the same basic language.

statistics and information theory

I got a couple nice books recently:

  1. Probability Theory: The Logic of Science
  2. Information Theory, Inference, and Learning Algorithms

Both these books are important to me, because the little statistics I tried to learn in university didn't make any sense. It wasn't for fear of math. I studied math. The stats I learned made vague sense when discussing uniform and discrete problems, but seemed increasingly mysterious as continuous non-uniform distributions were introduced: the justification for assigning a particular process to a particular distribution never seemed very clear, and the flow of information between knowns and unknowns, data and hypotheses, and the meaning of "randomness", became increasingly muddled. It resisted my ability to understand.

These books -- especially the former -- seem to place all that muddle in the context of a titanic struggle between Bayesian and Frequentist philosophical perspectives. Which is good. It's actually very important to me to see that there has been meaningful digression into the deeper epistemology of probability, because most statistics textbooks just pressure philosophical questions about the reasoning framework into humiliation and silence. These books come out plainly in favour of the Bayesian (knowledge-representation) view of probability, and give a pleasant contextualization of classical information theory in these terms. But they also spend a good deal of time discussing how a probabilistic reasoning process can be thought to make sense -- to be well-motivated and executed with confidence -- from the pragmatic needs of a creature needing to perform some uncertain reasoning.

I've heard people describe Bayesian inference as a cult. I'd be curious to hear that side of the argument distilled; so far it just seems like refreshingly clear thinking (similar to the clarity of thinking in Exploring Randomness, another one I've recently enjoyed).

cool language of the week

IBAL is a nice language for playing with inference in a way which is easy for programmers. Perhaps the future will see more such languages

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