Chapman & Hall, 2022. — 390 p. — ISBN 9781032105710. The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some...
World Scientific Publishing, 2024. — 380 p. — eBook ISBN 978-981-12-8492-2. Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis...
Springer, 2022. — 362 p. — (Springer Texts in Statistics). — ISBN 978-3-031-09839-0. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new...
Pearson Education, Inc., 2023. — 381 p. — ISBN-13 978-0-13-758098-9. Leverage the full power of Bayesian analysis for competitive advantage. Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and...
2nd edition. — O’Reilly Media, 2021. — 338 p. — ISBN 9781492089469. 2021-05-18: First Release If you know how to program with Python, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions instead of continuous mathematics. Once you get...
Wiley, 2012. — 486 p. — 4th ed. — ISBN: 1118332571
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee's book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.
This new fourth edition looks at...
Wiley, 2019. — 308 p. — (Quality & Reliability Engineering). — ISBN 9781119287971. Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches...
Springer, 2023. — 395 p. — ISBN 978-981-19-4754-4. This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that...
2nd. ed. — Boca Raton: CRC Press, 2020. — 611 p. — (Chapman & Hall/CRC Texts in Statistical Science). — ISBN: 036713991X. Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step...
SAGE Publications Ltd., 2022. — 272 p. — ISBN 978-1-5297-6861-9. This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with...
Springer Singapore, 2023. — 239 p. — eBook ISBN: 978-981-99-3838-4. Focuses on widely applicable information criterion (WAIC) & widely applicable Bayesian information criterion (WBIC) Presents 100 carefully selected exercises accompanied by solutions in the main text Contains detailed source programs and Stan codes to enhance readers’ grasp of the mathematical concepts...
No Starch Press, 2019. — 258 p. — ISBN: 978-1-59327-956-1. Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it.Bayesian Statistics the Fun...
СПб.: Питер, 2021. — 304 с. — (Библиотека программиста). 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 лекции! Эта книга даст вам полное понимание байесовской...
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