Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321795715> ?p ?o ?g. }
- W4321795715 abstract "Given a sequence X=(X1,X2,…) of random observations, a Bayesian forecaster aims to predict Xn+1 based on (X1,…,Xn) for each n≥0. To this end, in principle, she only needs to select a collection σ=(σ0,σ1,…), called “strategy” in what follows, where σ0(·)=P(X1∈·) is the marginal distribution of X1 and σn(·)=P(Xn+1∈·|X1,…,Xn) the nth predictive distribution. Because of the Ionescu–Tulcea theorem, σ can be assigned directly, without passing through the usual prior/posterior scheme. One main advantage is that no prior probability is to be selected. In a nutshell, this is the predictive approach to Bayesian learning. A concise review of the latter is provided in this paper. We try to put such an approach in the right framework, to make clear a few misunderstandings, and to provide a unifying view. Some recent results are discussed as well. In addition, some new strategies are introduced and the corresponding distribution of the data sequence X is determined. The strategies concern generalized Pólya urns, random change points, covariates and stationary sequences." @default.
- W4321795715 created "2023-02-25" @default.
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- W4321795715 date "2023-01-01" @default.
- W4321795715 modified "2023-09-23" @default.
- W4321795715 title "A Probabilistic View on Predictive Constructions for Bayesian Learning" @default.
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- W4321795715 doi "https://doi.org/10.1214/23-sts884" @default.
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