A Statistical Mechanical Interpretation of Algorithmic Information Theory
This book is the first one that provides a solid bridge between algorithmic information theory and statistical mechanics. Algorithmic information theory (AIT) is a theory of program size and recently is also known as algorithmic randomness. AIT provides a
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Kohtaro Tadaki
A Statistical Mechanical Interpretation of Algorithmic Information Theory 123
SpringerBriefs in Mathematical Physics Volume 36
Series Editors Nathanaël Berestycki, University of Vienna, Vienna, Austria Mihalis Dafermos, Mathematics Department, Princeton University, Princeton, NJ, USA Atsuo Kuniba, Institute of Physics, The University of Tokyo, Tokyo, Japan Matilde Marcolli, Department of Mathematics, University of Toronto, Toronto, Canada Bruno Nachtergaele, Department of Mathematics, Davis, CA, USA Hirosi Ooguri, Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
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Nathanaël Berestycki (University of Cambridge, UK) Mihalis Dafermos (University of Cambridge, UK / Princeton University, US) Atsuo Kuniba (University of Tokyo, Japan) Matilde Marcolli (CALTECH, US) Bruno Nachtergaele (UC Davis, US) Hirosi Ooguri (California Institute of Technology, US / Kavli IPMU, Japan)
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Kohtaro Tadaki
A Statistical Mechanical Interpretation of Algorithmic Information Theory
123
Kohtaro Tadaki Department of Computer Science Chubu University Kasugai, Japan
ISSN 2197-1757 ISSN 2197-1765 (electronic) SpringerBriefs in Mathematical Physics ISBN 978-981-15-0738-0 ISBN 978-981-15-0739-7 (eBook) https://doi.org/10.1007/978-981-15-0739-7 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or inf
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