This thesis presents a sharp approximation of the density of long runs of a random walk conditioned on its end value or by an average of a functions of its summands as their number tends to infinity. In the large deviation range of the conditioning event it extends the Gibbs conditional principle in the sense that it provides a description of the distribution of the random walk on long subsequences. An extension for the approximation of the conditional density in the multivariate case is provided. Approximation of the density of the runs is also obtained when the conditioning event states that the end value of the random walk belongs to a thin or a thick set with non void interior. The approximations hold either in probability under the conditional distribution of the random walk, or in total variation norm between measures. Application of the approximation scheme to the evaluation of rare event probabilities through Importance Sampling is provided. When the conditioning event is in the zone of the central limit theorem it provides a tool for statistical inference in the sense that it produces an effective way to implement the Rao-Blackwell theorem for the improvement of estimators; it also leads to conditional inference procedures in models with nuisance parameters. An algorithm for the simulation of such long runs is presented, together with an algorithm determining the maximal length for which the approximation is valid up to a prescribed accuracy.
