Reviews for Deterministic and Stochastic Error Bounds in Numerical Analysis

The average rating for Deterministic and Stochastic Error Bounds in Numerical Analysis based on 2 reviews is 3 stars.

Review # 1 was written on 2013-01-08 00:00:00 David Schwendinger Overall a much lighter introduction to meta-mathematics. The LISP expressions really drive home the ideas (at least for me, as a computer scientist). It does not require much background and is self-contained. The only thing I dislike is the author's tone when it comes to comparing his work with his contemporaries.

Review # 2 was written on 2016-03-06 00:00:00 Ernest Fontaine I normally don't do this, but I'm going to copy/paste this review across three separate books: Chaitin's "The Unknowable", "The Limits of Mathematics", and "Exploring Randomness". All three are all thin, overpriced, but very approachable books on Algorithmic Information Theory. Themes include: - Undecidability, as the basis of formulating a new kind of randomness measure for numbers that have already been generated (not just restricting randomness measures to the processes that generate numbers). - Chaitin's Omega constant, which is the probability that universal Turning machine will halt on random input. This constant is "maximally unknowable", but that doesn't stop one from performing math with it. - Philosophy surrounding these topics. One might underestimate Kolmogorov's contributions, given how ridiculously self-promoting Chaitin has been with this AIT. However, the topics are interesting enough that he's easy to forgive. And he uses many code demonstrations (LISP) to make concrete examples out of the math.