Mixtures of conjugate prior distributions and large deviations for level crossing probabilities
Claudio Macci, Lea Petrella · Cineca Institutional Research Information System (Tor Vergata University) · 2006
In this paper we present asymptotic estimates of level crossing probabilities from a Bayesian point of view, based on large deviations. For the Bayesian analysis we choose a finite mixture of conjugate prior distributions to model the uncertainty on the unknown parameters of the two classes of stochastic processes considered: the Brownian motion and the compound Poisson process with upward jumps and negative drift. The estimates of level crossing probabilities are derived as a consequence of large deviation principles for posterior distributions.