The thing is, there is no one true razor. Different sources have different associated reference machines—some are more like Turing Machines, others are more like CA. If what you are looking at is barcodes, then short ones are pretty rare—and if you go into simulated worlds, sources can have practically any distribution you care to mention.
Yes, you can model these as “complier overhead” constants—which represent the “cost” of simulating one reference machine in another—but that is just another way of saying you have to unlearn the Solomonoff prior and use another one—which is more appropriate for your source.
You can still do that, whatever your reference machine is—provided it is computationally universal—and doesn’t have too much “faith”.
The thing is, there is no one true razor. Different sources have different associated reference machines—some are more like Turing Machines, others are more like CA. If what you are looking at is barcodes, then short ones are pretty rare—and if you go into simulated worlds, sources can have practically any distribution you care to mention.
Yes, you can model these as “complier overhead” constants—which represent the “cost” of simulating one reference machine in another—but that is just another way of saying you have to unlearn the Solomonoff prior and use another one—which is more appropriate for your source.
You can still do that, whatever your reference machine is—provided it is computationally universal—and doesn’t have too much “faith”.