| 书目名称 | From Statistical Physics to Statistical Inference and Back | | 编辑 | Peter Grassberger,Jean-Pierre Nadal | | 视频video | | | 丛书名称 | Nato Science Series C: | | 图书封面 |  | | 描述 | Physicists, when modelling physical systems with a large numberof degrees of freedom, and statisticians, when performing dataanalysis, have developed their own concepts and methods for making the`best‘ inference. But are these methods equivalent, or not? What isthe state of the art in making inferences? The physicists wantanswers. More: neural computation demands a clearer understanding ofhow neural systems make inferences; the theory of chaotic nonlinearsystems as applied to time series analysis could profit from theexperience already booked by the statisticians; and finally, there isa long-standing conjecture that some of the puzzles of quantummechanics are due to our incomplete understanding of how we makeinferences. Matter enough to stimulate the writing of such a book asthe present one. .But other considerations also arise, such as the maximum entropymethod and Bayesian inference, information theory and the minimumdescription length. Finally, it is pointed out that an understandingof human inference may require input from psychologists. This livelydebate, which is of acute current interest, is well summarized in thepresent work. . | | 出版日期 | Book 1994 | | 关键词 | Bayesian inference; Chaos; Minimum Description Length; SPIN; Statistical Physics; Symbol; complexity; error | | 版次 | 1 | | doi | https://doi.org/10.1007/978-94-011-1068-6 | | isbn_softcover | 978-94-010-4465-3 | | isbn_ebook | 978-94-011-1068-6Series ISSN 1389-2185 | | issn_series | 1389-2185 | | copyright | Springer Science+Business Media Dordrecht 1994 |
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发表于 2025-3-21 17:34:02
