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  • Cited by 1537
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Zapata, Francisco Kosheleva, Olga and Kreinovich, Vladik 2019. Recent Developments in Data Science and Intelligent Analysis of Information. Vol. 836, Issue. , p. 90.

    Shaw, Victor N. 2019. Three Worlds of Collective Human Experience: Individual Life, Social Change, and Human Evolution. p. 181.

    Briggs, William M. Nguyen, Hung T. and Trafimow, David 2019. Structural Changes and their Econometric Modeling. Vol. 808, Issue. , p. 3.

    González-Gaitán, S de Souza, R S Krone-Martins, A Cameron, E Coelho, P Galbany, L and Ishida, E E O 2019. Spatial field reconstruction with INLA: application to IFU galaxy data. Monthly Notices of the Royal Astronomical Society, Vol. 482, Issue. 3, p. 3880.

    Shaw, Victor N. 2019. Three Worlds of Collective Human Experience: Individual Life, Social Change, and Human Evolution. p. 23.

    Lent, Craig S. 2019. Energy Limits in Computation. p. 1.

    Tame, Jeremy R. H. 2019. Approaches to Entropy. p. 49.

    Briggs, William M. 2019. Beyond Traditional Probabilistic Methods in Economics. Vol. 809, Issue. , p. 22.

    Mair, Sebastian and Brefeld, Ulf 2018. Distributed robust Gaussian Process regression. Knowledge and Information Systems, Vol. 55, Issue. 2, p. 415.

    Norton, John D. 2018. How to build an infinite lottery machine. European Journal for Philosophy of Science, Vol. 8, Issue. 1, p. 71.

    Killeen, Peter R. 2018. Predict, Control, and Replicate to Understand: How Statistics Can Foster the Fundamental Goals of Science. Perspectives on Behavior Science,

    Schön, Stephen Kermarrec, Gael Kargoll, Boris Neumann, Ingo Kosheleva, Olga and Kreinovich, Vladik 2018. Econometrics for Financial Applications. Vol. 760, Issue. , p. 266.

    Singh, Rajesh Ghosh, Dipanjan and Adhikari, R. 2018. Fast Bayesian inference of the multivariate Ornstein-Uhlenbeck process. Physical Review E, Vol. 98, Issue. 1,

    Młynarski, Wiktor F and Hermundstad, Ann M 2018. Adaptive coding for dynamic sensory inference. eLife, Vol. 7, Issue. ,

    Acton, Katherine A. and Baxter, Sarah C. 2018. Characterization of Random Composite Properties Based on Statistical Volume Element Partitioning. Journal of Engineering Mechanics, Vol. 144, Issue. 2, p. 04017168.

    Buede, Dennis M. Axelrad, Elise T. Brown, David P. Hudson, Daniel W. Laskey, Kathryn B. Sticha, Paul J. and Thomas, Jordan L. 2018. Inference enterprise models: An approach to organizational performance improvement. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 8, Issue. 6, p. e1277.

    Gabor, Thomas Kiermeier, Marie and Belzner, Lenz 2018. Digital Marketplaces Unleashed. p. 619.

    Cooke, James R. H. Selen, Luc P. J. van Beers, Robert J. and Medendorp, W. Pieter 2018. Bayesian adaptive stimulus selection for dissociating models of psychophysical data. Journal of Vision, Vol. 18, Issue. 8, p. 12.

    Knuth, Kevin H. 2018. Lattices and Their Consistent Quantification. Annalen der Physik, p. 1700370.

    Li, Hailong Bier, Markus Mars, Julian Weiss, Henning Dippel, Ann-Christin Gutowski, Olof Honkimäki, Veijo and Mezger, Markus 2018. Interfacial premelting of ice in nano composite materials. Physical Chemistry Chemical Physics,

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Book description

The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.

Reviews

‘This is not an ordinary text. It is an unabashed, hard sell of the Bayesian approach to statistics. It is wonderfully down to earth, with hundreds of telling examples. Everyone who is interested in the problems or applications of statistics should have a serious look.’

Source: SIAM News

'This book could be of interest to scientists working in areas where inference of incomplete information should be made.'

Source: Zentralblatt MATH

'… the author thinks for himself … and writes in a lively way about all sorts of things. It is worth dipping into it if only for vivid expressions of opinion. The annotated References and Bibliography are particularly good for this.'

Source: Notices of the American Mathematical Society

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