Visualization in Bayesian workflow
| Autor: | Andrew Gelman, Michael Betancourt, Daniel Simpson, Aki Vehtari, Jonah Gabry |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Statistics and Probability Model checking FOS: Computer and information sciences Economics and Econometrics Computer science Bayesian probability Posterior probability Inference computer.software_genre 010603 evolutionary biology 01 natural sciences Statistics - Applications Methodology (stat.ME) 03 medical and health sciences 0302 clinical medicine Applications (stat.AP) Statistical graphics Statistics - Methodology Markov chain Visualization Statistics::Computation Workflow ComputingMethodologies_PATTERNRECOGNITION Data mining Statistics Probability and Uncertainty computer 030217 neurology & neurosurgery Social Sciences (miscellaneous) |
| Popis: | Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high-dimensional models that are used by applied researchers. 17 pages, 11 Figures. Includes supplementary material |
| Databáze: | OpenAIRE |
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