| 期刊全称 | Bayesian Compendium | | 影响因子2023 | Marcel van Oijen | | 视频video | | | 发行地址 | Shows how Bayesian algorithms work in an easy to understand way.Explains Markov Chain Monte Carlo sampling with straightforward examples.Complemented with the R codes used in the book for modelling, d | | 图书封面 |  | | 影响因子 | .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...How exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion, etc). This book provides a short and easy guide to all of these and more. It was written from a unifying Bayesian perspective, which reveals how the multitude of techniques and approaches are in fact all related to one another. Basic notions from probability theory are introduced. Executable code examples are included to enhance the book’s practical use for scientific modellers, and all code is available online as well.. | | Pindex | Textbook 20201st edition |
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发表于 2025-3-21 19:22:59
