Bayesian prediction for stochastic processes. Theory and applications

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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Preprints, Working Papers, ...
18 pages. 2013


https://hal.archives-ouvertes.fr/hal-00750263
Contributor : Delphine Blanke <>
Submitted on : Saturday, December 28, 2013 - 3:10:49 PM
Last modification on : Monday, December 8, 2014 - 4:02:27 PM

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  • HAL Id : hal-00750263, version 2
  • ARXIV : 1211.2300

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Delphine Blanke, Denis Bosq. Bayesian prediction for stochastic processes. Theory and applications. 18 pages. 2013. <hal-00750263v2>

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