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Titlebook: Bayesian Compendium; Marcel van Oijen Textbook 20201st edition Springer Nature Switzerland AG 2020 Bayesian methods.Multidimensionality.Sa

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發(fā)表于 2025-3-21 19:22:59 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bayesian Compendium
影響因子2023Marcel van Oijen
視頻videohttp://file.papertrans.cn/182/181831/181831.mp4
發(fā)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
圖書封面Titlebook: Bayesian Compendium;  Marcel van Oijen Textbook 20201st edition Springer Nature Switzerland AG 2020 Bayesian methods.Multidimensionality.Sa
影響因子.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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Craniopharyngioma in Pediatrics and Adults,In science, we use models to help us learn from data. But we always work with incomplete theory and measurements that contain errors.
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Angio-architecture of the MedullaAs scientists, we want to know how to parameterise our models, make comparisons with other models, and quantify model predictive uncertainty. For all these purposes, measurement data are needed, but how exactly should we use the data? The answer is always the same: in the ..
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Angio-architecture of the MedullaThe Bayesian approach to parameter estimation requires modellers to make a major mental shift: we no longer aim to find a single ‘best’ parameter vector—instead we aim to determine the posterior probability distribution for the parameters.
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E. Kenneth Parkinson,W. Andrew YeudallIn this chapter, we discuss how multiple ‘competing’ models can be used simultaneously. There are advantages to having multiple different models, as was already recognized by Chamberlin in the 19th century (Chamberlin 1890).
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