Will_Newsome comments on Open Problems Related to Solomonoff Induction - Less Wrong

27 Post author: Wei_Dai 06 June 2012 12:26AM

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Comment author: Will_Newsome 06 June 2012 04:33:33PM 2 points [-]

Also keep in mind that algorithmic information/probability theory is actually quite hard to interpret correctly --- the basic, intuitive way to read meaning into the math is not quite the way to do it. cousin_it has a post or two correcting some intuitive errors of interpretation.

Comment author: Incorrect 06 June 2012 04:59:31PM *  1 point [-]
Comment author: Will_Newsome 06 June 2012 05:06:28PM *  3 points [-]

Alas, none of those are the relevant ones I think. I'm actually rather busy visiting home, so I can only justify certain comments to myself, but I hope someone provides the links.

For what it's worth, I'm a little skeptical of lukeprog's understanding of SI --- no offense to him meant, it's just I so happen to believe he made a rather big error when interpreting the math. On the other hand, cousin_it seems to be really on the ball here. Those are just my impressions; I'm a pretend philosopher, not a compsci dude. At any rate I think it'd be just dandy for cousin_it to check Luke's posts and share his impression or critiques.

Comment author: Will_Newsome 06 June 2012 05:23:57PM *  1 point [-]

Here's one I was thinking of:

The prior of a hypothesis does not depend on its complexity - cousin_it

(If I recall, Nesov's comment clearly demonstrates the important point.)

Comment author: timtyler 07 June 2012 12:40:23AM *  0 points [-]

That post seems to mix together the concept of a prior with the concept of experience.

Comment author: Will_Newsome 06 June 2012 06:35:58PM 0 points [-]
Comment author: Will_Newsome 06 June 2012 06:38:29PM *  1 point [-]

we can adopt the general rule that mentioning K-complexity in a discussion of physics is always a sign of confusion :-)

Mentioning it anywhere except algorithmic information theory is a sign of confusion. This includes theology and parapsychology. Use just Bayes or, if you want to be all fancy, updateless-like decision theories. I love algorithmic probability to death but it's just not something you should use casually. Too many pitfalls.

Comment author: Eugine_Nier 07 June 2012 02:04:49AM 0 points [-]

Use just Bayes

Bayes requires a prior.

Comment author: Will_Newsome 07 June 2012 03:57:37AM 2 points [-]

No one should ever need to discuss "priors". Focus on the likelihood ratio.

Comment author: timtyler 12 June 2012 01:10:59AM *  1 point [-]

...but that's like comparing apples and cheese!