Applied Bayesian and Classical Inference, F. Mosteller; D. L. Wallace

Автор: Nachtsheim;Neter;Kutner
Название: Applied Linear Statistical Models with Student CD
ISBN: 0071122214 ISBN-13(EAN): 9780071122214
Издательство: McGraw-Hill
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Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

Автор: Edited by Kim-Anh Do
Название: Bayesian Inference for Gene Expression and Proteomics
ISBN: 052186092X ISBN-13(EAN): 9780521860925
Издательство: Cambridge Academ
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Описание: The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.

Автор: Harney
Название: Bayesian Inference
ISBN: 3319416421 ISBN-13(EAN): 9783319416427
Издательство: Springer
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Описание: This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data.  New sections feature factorizing parameters, commuting parameters,  observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge.  Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.

Автор: Box, George E. P. Tiao, George C.
Название: Bayesian inference in statistical analysis
ISBN: 0471574287 ISBN-13(EAN): 9780471574286
Издательство: Wiley
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Описание: Designed to form the basis of a graduate course on Bayesian inference, this textbook discusses important general issues of the Bayesian approach. It investigates problems, illustrating the appropriate analysis of mathematical results with numerical examples.

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.

Автор: Pavel V. Shevchenko
Название: Modelling Operational Risk Using Bayesian Inference
ISBN: 3642423531 ISBN-13(EAN): 9783642423536
Издательство: Springer
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Цена: 16769.00 р.
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Описание: This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses.

Автор: Peter M?ller; Brani Vidakovic
Название: Bayesian Inference in Wavelet-Based Models
ISBN: 0387988858 ISBN-13(EAN): 9780387988856
Издательство: Springer
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Описание: Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored.

Автор: Hanns L. Harney
Название: Bayesian Inference
ISBN: 364205577X ISBN-13(EAN): 9783642055775
Издательство: Springer
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Цена: 13270.00 р.
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Описание: Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. The text is written at introductory level, with many examples and exercises.