Solomonoff induction is uncomputable, so it's not going to help you in any way.
But Jaynes (who was a physicist) said that using Bayesian methods to analyze magnetic resonance data helped him gain an unprecedented resolution. Quoting from his book:

In the 1987 Ph.D. thesis of G. L. Bretthorst, and more fully in Bretthorst (1988), we applied Bayesian analysis to estimation of frequencies of nonstationary sinu- soidal signals, such as exponential decay in nuclear magnetic resonance (NMR) data, or chirp in oceanographic waves. We found – as was expected on theoretical grounds – an improved resolution over the previously used Fourier transform methods. If we had claimed a 50% improvement, we would have been believed at once, and other researchers would have adopted this method eagerly. But, in fact, we found orders of magnitude improvement in resolution.

jacob_cannell above seems to think it is very important for physicists to know about Solomonoff induction.

Solomonoff induction is one of those ideas that keeps circulating here, for reasons that escape me.

If we are talking about Bayesian methods for data analysis, almost no one on LW who is breathlessly excited about Bayesian stuff actually knows what they are talking about (with 2-3 exceptions, who are stats/ML grad students or up). And when called on it retreat to the "Bayesian epistemology" motte.

Bayesian methods didn't save Jaynes from being terminally confused about causality and the Bell inequalities.

Open thread, Dec. 21 - Dec. 27, 2015

by MrMind 1 min read21st Dec 2015233 comments


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