Comparison of the Gaussian process inference to the true optimum and a parametric estimate.
Comparison across 100 simulations under the policies inferred from each approach show the nearly optimal performance of the GP and the tragic crashes introduced by the parametric management.
Working through an exploratory sensitivity analysis to see GP performance over different parameters.
Distribution of yield over replicates shows the parametric model performing rather poorly, while most of the GP replicates perform nearly optimally.
from the commit log
- smaller r, shows GP outperform parametric estimate 09:21 am 2012/12/21
- small noise. works well. 11:01 pm 2012/12/20
- GP performs poorly with weak noise prior (nugget) when not conditioned on 0,0 10:45 pm 2012/12/20
- less successful with higher noise level 09:10 pm 2012/12/20
- Ah here we go! with a decent allee effect model, the results are much clearer! 08:52 pm 2012/12/20
- strange Ricker Allee example – even true model doesn’t harvest successfully 08:37 pm 2012/12/20
- delta 1.5, r 1.3 K 4.5 08:17 pm 2012/12/20
- higher K value results in better persistence in general, not always outperforming parametric model though. 07:57 pm 2012/12/20
- harvest, and then escaped population recruits; comparable 07:09 pm 2012/12/20
- shorter OptTime and lower initial state do not much change the fraction that fail to persist 06:31 pm 2012/12/20
- Sensitivity analysis over replicates shows mixed results 05:35 pm 2012/12/20
- fixed bug (had Ef and V in gp_transition_matrix) 05:18 pm 2012/12/20
- still crashing 05:01 pm 2012/12/20
- more iterations 04:34 pm 2012/12/20
- corrected iterations 04:28 pm 2012/12/20
- cpu=8 for running on one.ucdavis 02:21 pm 2012/12/20
- unique seeds for parallel computing 02:11 pm 2012/12/20
- 30 replicates with full graphs 02:03 pm 2012/12/20
- avoid external source call, print plots 12:49 pm 2012/12/20
- trying methods for looping over replicates efficiently 12:38 pm 2012/12/20