Solomonoff's induction has been argued to be the computational formalization of pure Bayesianism. To understand, recall that Bayesianism derives the posterior probability formula_1 given data formula_2 by applying Bayes rule, which yields formula_3, where theories formula_4 are alternatives to theory formula_5. For this equation to make sense, the quantities formula_6 must be well-defined for all theories formula_5 and formula_4. In other words, any theory must define a probability distribution over observable data formula_2. Solomonoff's induction essentially boils down to demanding in addition that all such probability distributions be computable.