### prosecutor

- Bit more writing on comment reply. Sent to Alex and Alan for feedback.

### nonparametric-bayes

Demonstrating robustness of the GP approach is remarkably difficult ironically because the comparison methods are not particularly robust; most often due to poor initial conditions. Doing all I can to make the MLE and parametric Bayesian cases suitably automated such that the can give reasonable performance as I loop over various simulation parameters and models without much hand-holding of each of the estimators.

Also an automatic sensitivity analysis gets pretty computationally intensive and generates a lot of different outputs to keep track of, trying to improve this through cleaner code, parallelization, caching, chunk dependencies, and namespaces for scripts.

- Playing with mcmc convergence over initial conditions
- Running case without measurement error for parametric bayesian models
- Adaptuing runs for parallel and for execution on farm cluster
- Consider ADMB for the MLE estimate to be a bit more robust for automated cases.

### Log

- process noise only example (first attempt) 04:50 pm 2013/05/24
- Consider the case of process noise only in the parametric bayesian versions 03:38 pm 2013/05/24
- parallel jags 03:31 pm 2013/05/24