Unlike the traditional universal prior 
, the Speed Prior 
is
recursively approximable with arbitrary precision. This allows
for deriving an asymptotically optimal recursive way of
computing predictions, based on a natural discount of the probability
of data that is hard to compute by any method.
This markedly contrasts with Solomonoff's
traditional noncomputable approach to optimal prediction based on
the weaker assumption of recursively computable priors that completely
ignore resource limitations [24,25].
Our expected loss bounds building on Hutter's recent work
show that -based prediction is quite accurate as
long as the true unknown prior is less dominant than , reflecting
an observation-generating process on some unknown computer
that is not optimally efficient.
Assuming that our universe is sampled from a prior that does
not dominate we obtain several nontrivial predictions
for physics.
Acknowledgment. The material presented here is based on section 6 of [22]. Thanks to Ray Solomonoff, Leonid Levin, Marcus Hutter, Christof Schmidhuber, and an unknown reviewer, for useful comments.