| Routledge

44,970원  | 20250429  | 9781032744001

Bayesian Statistics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inference, contrasting it with classical statistical methods like null hypothesis significance testing.

 | Routledge

303,750원  | 20250429  | 9781032746388

Bayesian Statistics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inference, contrasting it with classical statistical methods like null hypothesis significance testing.

 |

315,000원  | 20250626  | 9781032451909

Provides an approachable introduction for researchers who are new to Bayes, meta-analysis, or both. There is an emphasis on hands-on learning using a variety of software packages.

 |

107,970원  | 20250626  | 9781032451893

Provides an approachable introduction for researchers who are new to Bayes, meta-analysis, or both. There is an emphasis on hands-on learning using a variety of software packages.

 | Dale Mark Nesbitt

110,980원  | 20250613  | 9780578341323

Bayesian Linear Regression Made ClearIf you've ever used regression software and wondered what the numbers really mean, this book is for you. Dale Mark Nesbitt breaks down Bayesian Linear Regression step-by-step, explaining the math and logic in a way that's both practical and easy to follow.You'll learn how Bayesian methods extend classical regression, build probability models from data, incorporate your own judgment, and improve predictions.

 | Indy Pub

129,480원  | 20250613  | 9798349419720

Bayesian Linear Regression Made ClearIf you've ever used regression software and wondered what the numbers really mean, this book is for you. Dale Mark Nesbitt breaks down Bayesian Linear Regression step-by-step, explaining the math and logic in a way that's both practical and easy to follow.You'll learn how Bayesian methods extend classical regression, build probability models from data, incorporate your own judgment, and improve predictions.

 | Springer Nature B.V.

73,980원  | 20230324  | 9781484290644

This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.The book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics.

Roman Garnett  | Cambridge University Press

59,000원  | 20230209  | 9781108425780

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations.

Berger, James O., Bernardo, Jose-Miguel, Sun, Dongchu  | World Scientific Publishing Company

199,800원  | 20250131  | 9789811284908

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 of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics.

 | CRC Press

114,720원  | 20210930  | 9781032175003

This book shows how to analyze spatial and spatial-temporal data. It focuses on key datasets and data analysis, using the open source software WinBUGS, R, and GeoDa. It examines a range of different spatial and spatial-temporal data modeling situations encountered in the social and economic sciences.

Oijen, Marcel van  | Springer

129,480원  | 20210919  | 9783030558994

This book describes how Bayesian methods work. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures.

 | Draft2Digital

49,950원  | 20211222  | 9798201740498

Bayesian methodology differs from traditional statistical methodology which involves frequentist approach. Bayesian methodology was introduced by Thomas Bayes (Statistician and minister at the Presbyterian Chapel) during the 18th Century. Bayesian methodology is now widely being used due to its simple, straightforward and interpretable characteristics of probability values and the efficiency of modern day computer systems.

 |

0원  | 20250523  | 9781138626515

This book provides an overview of Bayesian econometric models both for students/young researchers who are learning these models for the first time, and for more experienced researchers who would like to use the models in a Bayesian framework. The book is written so as to be accessible to these dual audiences, and is filled with illustrative worked examples using real data. The book uses Stan - developed by all but one of the authors - to implement the models. Stan is a powerful and flexible probabilistic programming language that can handle the very complex models found in econometrics.

 | Springer Nature B.V.

73,980원  | 20200924  | 9783030558987

This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn't difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures.

Bovens, Luc  | Oxford University Press

259,000원  | 20210101  | 9780199269754

Probability theory is increasingly important to philosophy. Bayesian probabilistic models offer us ways of getting to grips with fundamental problems about information, coherence, reliability, confirmation, and testimony, and thus show how we can ...