Titlebook: Bayesian Compendium; Marcel van Oijen Textbook 2024Latest edition The Editor(s) (if applicable) and The Author(s), under exclusive license

没花的是打扰

Marcel van OijenCovers process-based models as well as simple regression and shows how Bayesian algorithms work in an accessible way.Includes chapters on model emulation, graphical modelling, hierarchical modelling,

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Paradox

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The Algebraic Structure of MetagraphsIn the examples of MCMC in the preceding chapter, no prior or likelihood was specified, nor was there any talk of a posterior distribution. So why is MCMC important for Bayesian analysis?

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Metagraphs in Workflow and Process AnalysisThis chapter answers a number of common questions about Bayesian calibration in general and MCMC in particular. Many topics are addressed elsewhere in this book at greater length (and pointers to the chapters are then given), but some are only addressed briefly here.

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Metagraphs in Workflow and Process AnalysisThis chapter discusses what needs to be done after your Bayesian calibration: how to interpret your results, what to report and how to report it with emphasis on visualisation.

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Metagraphs in Data and Rule ManagementFitting a straight line through data can be done in many ways that may seem different at first, but after closer inspection prove to be based on the same mathematics. In this chapter, we shall fit a line to data in 13 different ways and compare the resulting parameter estimates. So our goal is to estimate the intercept and the slope of the line.

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