Bayesian inference for stochastic processes (1st)

This volume introduces Bayesian inference procedures for stochastic processes.

There are clear advantages to the Bayesian approach (including the optimal use of prior information).

Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space.

The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes.