ABSTRACT
The Markov Chain Monte Carlo (MCMC) parameter estimation is an efficient computational approach for nonlinear parameter estimation. This chapter applies MCMC parameter estimation to empirical and differential models with functions from the demodelr package. An organizational workflow is introduced to aid in organization of model processes and products. Emphasis is placed on interpretation of parameter estimates from the MCMC method and visualizing parameter results with histograms.
