Bayesian prediction for stochastic processes. Theory and applications

Delphine Blanke; Denis Bosq

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arXiv · 1211.2300

Bayesian prediction for stochastic processes. Theory and applications

Delphine Blanke·Denis Bosq

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Abstract

In this paper, we adopt a Bayesian point of view for predicting real continuous-time processes. We give two equivalent definitions of a Bayesian predictor and study some properties: admissibility, prediction sufficiency, non-unbiasedness, comparison with efficient predictors. Prediction of Poisson process and prediction of Ornstein-Uhlenbeck process in the continuous and sampled situations are considered. Various simulations illustrate comparison with non-Bayesian predictors.

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Delphine Blanke, Denis Bosq. 2013-12-28. Bayesian prediction for stochastic processes. Theory and applications. https://arxiv.org/abs/1211.2300

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