Bayesian Forecasting via Deterministic Model

Roman Krzysztofowicz

Risk Analysis, 1999, vol. 19, issue 4, 739-749

Abstract: Rational decision making requires that the total uncertainty about a variate of interest (a predictand) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Supposethe state‐of‐knowledge is embodied in a deterministic model, which is imperfect and outputs only an estimate of the predictand. Fundamentals are presented of two Bayesian methods for producing a probabilistic forecast via anydeterministic model. The Bayesian Processor of Forecast (BPF) quantifies the total uncertainty in terms of a posterior distribution, conditional on model output. The Bayesian Forecasting System (BFS) decomposes the total uncertainty into input uncertainty and model uncertainty, which are characterized independently and then integrated into a predictive distribution. The BFS is compared with Monte Carlo simulation and “ensemble forecasting” technique, none of which can alone produce a probabilistic forecast that quantifies the total uncertainty, but each can serve as a component of the BFS.

Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:19:y:1999:i:4:p:739-749

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