In this paper we develop a Bayesian analysis for the nonlinear regression model with errors that follow a continuous autoregressive process. In this way, unequally spaced observations do not present a problem in the analysis. We employ the Gibbs sampler, (see Gelfand, A., Smith, A. (1990). Sampling based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85:398-409.), as the foundation for making Bayesian inferences. We illustrate these Bayesian inferences with an analysis of a real data-set. Using these same data, we contrast the Bayesian approach with a generalized least squares technique.
- Continuous autoregressive process
- Gibbs sampler
- Metropolis-Hastings algorithm within Gibbs sampler
- Nonlinear models