Bayesian prediction for stochastic processes. Theory and applications
Statistics Theory
2013-12-31 v2 Statistics Theory
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.
Cite
@article{arxiv.1211.2300,
title = {Bayesian prediction for stochastic processes. Theory and applications},
author = {Delphine Blanke and Denis Bosq},
journal= {arXiv preprint arXiv:1211.2300},
year = {2013}
}
Comments
18 pages