**From:** Ben Goertzel (*ben@goertzel.org*)

**Date:** Thu May 29 2003 - 05:22:40 MDT

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*> What I'd like to know is: what do the two Bayesianisms have
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*> to do with each other? What is the connection between
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*> an enthusiasm for conditional probabilities, and a belief that
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*> probabilities are subjective?
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Well, the latter is a subset of the former ;-)

Going a little deeper.... In a mundane, data-analysis context, non-Bayesian

probabilists usually wind up making distributional assumptions before

approaching a dataset (assuming things are Gaussian, for example).

Bayesians, instead of making generic distributional assumptions, make

assumptions regarding prior distributions. (Thus Bayesianism involves

"nonparametric statistics") You've got your choice: make a generic

("objective", but maybe wrong) assumption or a particular (subjective)

assumption.

Bayesianism gives the opportunity for deeper nonprobabilistic principles to

be invoked -- e.g. Occam's razor a la algorithmic information theory, where

one assumes that computationally simpler entities have higher a priori

probability. In this sense Bayesianism is more "open" than conventional

uses of probability/statistics. Which is why Novamente's use of probability

theory is Bayesian in nature -- because I believe probability theory as used

in the mind is generally "open" in this way... making use of prior

assumptions derived from nonprobabilistic aspects of mind, rather than

making use of assumptions regarding distributional form. (Although in a

perception/action context, some parts of the human mind/brai probably make

use of distributional assumptions as well).

-- Ben

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