An anytime algorithm for mixing the computable measures

Epistemic status: Not peer-reviewed, high chance of typos and small chance of errors. Written entirely by me, checked by Fable. In this post I prove the existence of an anytime computable Bayesian mixture of all computable measures called , and briefly argue that this is a reasonable alternative to Solomonoff induction's universal distribution for general sequence prediction. I believe Tom Sterkenburg told me that this is possible, but I could not find it written down anywhere (though I may have missed it!). Indeed, has been conjectured not to be limit=anytime computable by Hutter and Muchnik: