On Sep 10, 5:06 pm, Brent Meeker <[EMAIL PROTECTED]> wrote:

>
> Yes there is.  In fact descriptions with fewer free parameters are 
> automatically
> favored by Bayesian inference.
>
> http://quasar.as.utexas.edu/papers/ockham.pdf
>
> Brent Meeker
>

Nice try.  That's an interesting paper, but it's merely one guys
attempt to try to define the problem in terms of Bayesianism.  It does
not provide solutions to (a) and (b), which remain unresolved.

These types of attempts to try to reduce Occam's razor to Bayes soon
run into a big big problem, which I have already mentioned:

There is more than one meaure of complexity.  For example,
*information* is not the same thing as *knowledge*.  Shannon
information is simply a measure of the degree of randomness in a
string, whereas *knowledge* is more a measure of the amount of work
that went into producing a string (ie it is *meaningful* information).

Effective use of Occam's razor also requires us to judge the
simplicity/complexity of *meaningful information* (ie knowledge), not
just Shannon information.  Bayesianism Induction cannot possibly do
this, since it cannot handle the *semantics* (meaning) of the
information, only the Shannon information.  This it is because it only
deals with the *functional* aspects of information... ie patterns as
they appear to external observers, rather than what the patterns
signify ( the *semantic* aspects of information).
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