The concepts of filtering, smoothing and predictions, are introduced in a user-friendly way by using a special case of the state-space model: the local trend model. The chapter begins by developing the foundation needed for deriving the recursive Kalman filter algorithm. After the brief introduction in Section 11.1, the ... click here for more details.

This chapter explains a Bayesian computational toolbox for a wide range of empirical finance models. It starts with the basic idea of Markov Chain simulation and its relationship with Bayesian Inference. While the discussion mainly focuses at the Gibbs Sampler, some alternative ways such as Metropolis and griddy Gibbs are ... click here for more details.