Algorithmic Probability and Friends. Bayesian Prediction and by David L. Dowe (auth.), David L. Dowe (eds.)

By David L. Dowe (auth.), David L. Dowe (eds.)

Algorithmic likelihood and associates: court cases of the Ray Solomonoff eighty fifth memorial convention is a suite of unique paintings and surveys. The Solomonoff eighty fifth memorial convention was once held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his quite a few pioneering works - such a lot quite, his innovative perception within the early Nineteen Sixties that the universality of common Turing Machines (UTMs) can be used for common Bayesian prediction and synthetic intelligence (machine learning). This paintings maintains to more and more impression and under-pin records, econometrics, computing device studying, information mining, inductive inference, seek algorithms, facts compression, theories of (general) intelligence and philosophy of technological know-how - and functions of those parts. Ray not just expected this because the route to real synthetic intelligence, but additionally, nonetheless within the Sixties, expected phases of growth in laptop intelligence which might finally result in machines surpassing human intelligence. Ray warned of the necessity to expect and talk about the aptitude results - and hazards - quicker instead of later. in all likelihood foremostly, Ray Solomonoff was once an excellent, satisfied, frugal and adventurous person of mild get to the bottom of who controlled to fund himself whereas electing to behavior quite a bit of his paradigm-changing study outdoors of the college method. the quantity includes 35 papers relating the abovementioned issues in tribute to Ray Solomonoff and his legacy.

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Extra info for Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011

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Computer J. 11(2), 185–194 (1968) 189. : An invariant Bayes method for point estimation. Classification Society Bulletin 3(3), 11–34 (1975) 190. : Intrinsic classification by MML - the Snob program. In: Proc. 7th Australian Joint Conf. on Artificial Intelligence, pp. 37–44. World Scientific (November 1994) 191. : Minimum message length and Kolmogorov complexity. Computer J. 42(4), 270–283 (1999) 192. : Refinements of MDL and MML coding. Computer Journal 42(4), 330–337 (1999) 193. : Rejoinder. Computer Journal 42(4), 345–347 (1999) 194.

236–249. Springer, Heidelberg (2013) 81. : Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973) 82. : Universal heuristics: How do humans solve “Unsolvable” problems? L. ) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 53–54. Springer, Heidelberg (2013) 83. : Scriven on human unpredictability. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition 17(5), 69–74 (1966) 84. : Partial match distance. L. ) Solomonoff Festschrift.

The probability of “undefined” (non-converging) output in generating the universal probability distribution. Information Processing Letters 106(6), 238–240 (2007) 160. : Three kinds of probabilistic induction: Universal distributions and convergence theorems. Computer Journal 51(5), 566–570 (2008); Christopher Stewart WALLACE (1933-2004) Memorial Special Issue 161. : Algorithmic probability: Theory and applications. , Emmert-Streib, F. ) Information Theory and Statistical Learning. Springer Science and Business Media, pp.

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