New York: Springer, 2017. — 552 p. This book presents operational modal analysis (OMA), employing a coherent and comprehensive Bayesian framework for modal identification and covering stochastic modeling, theoretical formulations, computational algorithms, and practical applications. Mathematical similarities and philosophical differences between Bayesian and classical...
Valencia: Valencia University Press, 1980. — 647 p. At conferences devoted to the foun(lations of proDability and statistics, it is natural that attention siould focus on points of division between supporters of rival schools of tiouglt. The resulting confrontation of ideas and personalities in sucll contexts is often stimulating and useful in sharpening perceptions about...
2nd ed. — Wiley, 2007. — 463 p. The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods...
N.-Y.: Wiley, 2009. — 336 p.
A hands-on introduction to computational statistics from a Bayesian point of view
Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the...
New York: John Wiley & Sons, 1992. — 600 p. Nature of Bayesian inference Standard normal theory inference problems Bayesian assessment of assumptions 1. Effect of non-normality Bayesian assessment of assumptions 2. Comparison of variances Random effect models Analysis of cross classification designs Inference about means with information from more than one source Some aspects...
Oxford: Oxford University Press, 2019. — 430 p. Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on...
Second Edition. — Chapman & Hall/CRC, 2004. — (Тexts in Statistical Science). Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide...
Springer, 2006. — 355 p. This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical...
Springer, 2003. — 310 p.
Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a...
Amsterdam: North-Holland, 1986. - 254 p.
The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians...
Elsevier, 1984. — 326 p.
Robustness is a fundamental issue for all statistical analyses; in fact it might be argued that robustness is the subject of statistics. In Bayesian statistics, the prior distribution can be seen as weighting the possible values of the parameter by their probability. The studies reported in this volume concern the sensitivity of Bayesian analyses to the...
Cambridge: Cambridge University Press, 1999. — 346 p. This exposition of the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students is unique in presenting its subject with a practical flavor and an emphasis on mainstream statistics. It shows how to infer scientific, medical, and social conclusions from numerical data. The authors...
Philadelphia: SIAM, 1987. - 91p.
A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics.
Chapman & Hall, 2003. — 323 p. Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients,...
New York: Springer, 2021. — 475 p. Design Research uses scientific methods to evaluate designs and build design theories. This book starts with recognizable questions in Design Research, such as A/B testing, how users learn to operate a device and why computer-generated faces are eerie. Using a broad range of examples, efficient research designs are presented together with...
New York: Packt Publishing, 2017. — 325 p. Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian...
Amsterdam: North-Holland, 1980. — 485 p. The main objective of this volume is to honour Sir Harold Jeffreys for the major theoretical and applied contributions he made to Bayesian analysis. Leading experts in the field, such as S. Geisser, I.J. Good and D.V. Lindley review and comment on these contributions.
Пер. с анг. В. А. Яроцкого. — М.: ДМК Пресс, 2018. — 184 с.: ил. — ISBN: 978-5-97060-664-3. Если вы знаете, как программировать на Python, и немного знаете о теории вероятности, значит, вы готовы освоить байесовскую статистику. Эта книга расскажет вам, как решать статистические задачи с помощью языка Python вместо математических формул и использовать дискретные вероятностные...
Пер. с англ. А. Павлов. — СПб.: Питер, 2021. — 304 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1655-3. Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские...
Учебное пособие. — Перевод с англ. А.А. Рывкина. — М.: Финансы и статистика, 1987. — 335 с. — (Библиотечка иностранных книг для экономистов и статистиков). В основе книги лежит концепция байесовского использования априорной информации в сочетании с накапливаемыми результатами наблюдений для выработки рациональных решений. Изложенные математические методы используются далее в...
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