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

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Graphs, Hypergraphs, and Metagraphsfundamentally different from the simpler models we studied in the previous chapters; we can still write them as functions . of their input consisting of covariates . and parameters .. But the output . from the models will be multivariate, e.g. time series of different properties of an ecosystem. Tha

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Metagraphs in Workflow and Process Analysisata likelihood function. The posterior distribution is then fully determined, and it encapsulates everything of interest. With the posterior in hand, we can make predictions with proper uncertainty quantification, we can carry out risk analysis and provide decision support. So the basic ideas are ex

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The Algebraic Structure of Metagraphs(2) information about the nodes. So the graph is just the visible part of the model. GMs do not represent a new kind of statistical model; they are just helpful tools for analysing joint probability distributions. Every distribution can be represented by a GM, so whatever your research problem or mo

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Erik Cuevas,Alberto Luque,Beatriz Riverapproach allows us to quantify predictive uncertainty when we use our models for prediction. And this is of course important for the user of these predictions, whether that user is us or someone to whom we report our results. Our probabilistic predictions allow calculation of risks and, more generall

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Prajna Kunche,K. V. V. S. Reddyred, . (BDT) (Berger, . (2nd ed.). Springer Series in Statistics. Springer, 1985; Jaynes, .. Cambridge University Press, 2003; Lindley, . (2nd ed.). Wiley, 1991; Van Oijen and Brewer, ., SpringerBriefs in Statistics. Springer International Publishing, 2022; Williams and Hooten (Ecol Appl 26:1930–194