In this thesis, we propose a new recursive algorithm, namely the model walking algorithm, to modify the widely used Occam''s window method in Bayesian model averaging procedure. It is verified, by simulation, that in the regression models, the proposed method is much more efficient in terms of computing time and the selected candidate models. Moreover, it is not sensitive to the initial models. We then apply Bayesian model averaging to the multiple longitudinal regression models with AR(1) random errors within subjects. Gibbs sampling method together with the model walking algorithm are employed. The proposed method is also successfully used to make rainfall prediction based on typhoon data in Taipei, Taiwan.