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

HALL

表两个

单纯

繁重

积云

MCMC and Multivariate Models,t does not affect the principles of Bayesian calibration in any way but may complicate its execution. In this chapter, we illustrate these issues with a quite simple PBM that as output produces two time series: the growth over time of the biomass and leaf area of vegetation.

Chronic

Gaussian Processes and Model Emulation,ckly. However, its output cannot be exactly the same as that of the original model, so it just provides an approximation. If the surrogate model is a statistical model that produces not just the approximative prediction of what the original model would have produced, but a whole probability distribution, then it is called a ., or just . for short.

Cardioversion

掺和

Metagraphs and Their Applications. A prior expresses uncertainty arising from incomplete knowledge, and whatever the subject is, people have different knowledge and expertise. So instead of speaking of ’the prior probability of .’, each of us should say ’my prior probability for .’. We . a prior probability distribution; we do not

defeatist

neutral-posture

Metagraphs in Data and Rule Managements the information content of our data. So all that is left is to apply Bayes’ Theorem (Eq. (.)) to derive our desired posterior distribution. Note that when talking about the posterior, we use the phrase ‘deriving the’ distribution rather than ‘assigning a’ distribution. That is because Bayes’ Theor