Occam's razor
There have also been other attempts to derive Occam's razor from probability theory, including notable attempts made by Harold Jeffreys and E. T. Jaynes. The probabilistic (Bayesian) basis for Occam's razor is elaborated by David J. C. MacKay in chapter 28 of his book "Information Theory, Inference, and Learning Algorithms", where he emphasizes that a prior bias in favor of simpler models is not required.