Bayesian Thinking, Modeling and Computation, Dey, Dipak K.

Автор: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 52790.00 T
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Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.

Автор: Dirk P. Kroese; Joshua C.C. Chan
Название: Statistical Modeling and Computation
ISBN: 149395332X ISBN-13(EAN): 9781493953325
Издательство: Springer
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Цена: 93150.00 T
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Описание: This book provides an introduction to modern statistics. It also offers an integrated treatment of mathematical statistics and statistical computation, emphasizing statistical modeling, computational techniques, and applications.

Автор: Kroese Dirk P
Название: Statistical Modeling and Computation
ISBN: 1461487749 ISBN-13(EAN): 9781461487746
Издательство: Springer
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Цена: 234490.00 T
Наличие на складе: Нет в наличии.
Описание: This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models.

Автор: Martin, Osvaldo A. (conicet And Aalto University) Kumar, Ravin Lao, Junpeng
Название: Bayesian modeling and computation in python
ISBN: 036789436X ISBN-13(EAN): 9780367894368
Издательство: Taylor&Francis
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Цена: 76550.00 T
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Описание: Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory.

Автор: Darwiche
Название: Modeling and Reasoning with Bayesian Networks
ISBN: 1107678420 ISBN-13(EAN): 9781107678422
Издательство: Cambridge Academ
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Цена: 65470.00 T
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Описание: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.

Название: Monte Carlo Methods in Bayesian Computation. M.-H. Chen, Q.-M. Shao, J.G. Ibrahim.
ISBN: 146127074X ISBN-13(EAN): 9781461270744
Издательство: Springer
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Цена: 153720.00 T
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Описание: Sampling from the posterior distribution and computing posterior quanti- ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput- ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv- ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste- rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in- volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac- tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications.

Автор: Fred J. Hickernell, Peter Kritzer
Название: Multivariate Algorithms and Information-Based Complexity
ISBN: 3110633116 ISBN-13(EAN): 9783110633115
Издательство: Walter de Gruyter
Цена: 128870.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

The series is devoted to the publication of high-level monographs, surveys and proceedings which cover the whole spectrum of computational and applied mathematics.

Автор: Tan, Ming T. , Tian, Guo-Liang , Ng, Kai Wang
Название: Bayesian Missing Data Problems
ISBN: 0367385309 ISBN-13(EAN): 9780367385309
Издательство: Taylor&Francis
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Цена: 65320.00 T
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Описание:

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-world problems, the authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms.

After introducing the missing data problems, Bayesian approach, and posterior computation, the book succinctly describes EM-type algorithms, Monte Carlo simulation, numerical techniques, and optimization methods. It then gives exact posterior solutions for problems, such as nonresponses in surveys and cross-over trials with missing values. It also provides noniterative posterior sampling solutions for problems, such as contingency tables with supplemental margins, aggregated responses in surveys, zero-inflated Poisson, capture-recapture models, mixed effects models, right-censored regression model, and constrained parameter models. The text concludes with a discussion on compatibility, a fundamental issue in Bayesian inference.

This book offers a unified treatment of an array of statistical problems that involve missing data and constrained parameters. It shows how Bayesian procedures can be useful in solving these problems.

Автор: Rakhee Kulshrestha
Название: Mathematical modeling and computation of real-time problems
ISBN: 0367517434 ISBN-13(EAN): 9780367517434
Издательство: Taylor&Francis
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Цена: 163330.00 T
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Описание: This book covers an interdisciplinary approach for understanding mathematical modeling by offering a collection of models, solved problems related to the models the methodologies employed, and the results using projects and case studies with insight into the operation of substantial real-time systems.

Автор: Tan, Ming T.
Название: Bayesian Missing Data Problems
ISBN: 142007749X ISBN-13(EAN): 9781420077490
Издательство: Taylor&Francis
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Цена: 117390.00 T
Наличие на складе: Нет в наличии.

Автор: Prado, Raquel (university Of California, Santa Cruz, California, Usa) Ferreira, Marco A. R. (virginia Tech, Blacksburg, Usa) West, Mike (duke Universi
Название: Time series
ISBN: 1032040041 ISBN-13(EAN): 9781032040042
Издательство: Taylor&Francis
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Цена: 45930.00 T
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Автор: Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G.
Название: Monte Carlo Methods in Bayesian Computation
ISBN: 0387989358 ISBN-13(EAN): 9780387989358
Издательство: Springer
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Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches.The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.