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- W4367000421 abstract "This paper reviews the growing field of Bayesian prediction. Bayes point and interval prediction are defined and exemplified and situated in statistical prediction more generally. Then, four general approaches to Bayes prediction are defined and we turn to predictor selection. This can be done predictively or non-predictively and predictors can be based on single models or multiple models. We call these latter cases unitary predictors and model average predictors, respectively. Then we turn to the most recent aspect of prediction to emerge, namely prediction in the context of large observational data sets and discuss three further classes of techniques. We conclude with a summary and statement of several current open problems." @default.
- W4367000421 created "2023-04-27" @default.
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- W4367000421 date "2023-04-24" @default.
- W4367000421 modified "2023-09-25" @default.
- W4367000421 title "A Cheat Sheet for Bayesian Prediction" @default.
- W4367000421 doi "https://doi.org/10.48550/arxiv.2304.12218" @default.
- W4367000421 hasPublicationYear "2023" @default.
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