Discussed potential solution to the model choice scenario:
- Consider an ordered list of models, A, B, C, D
- Then the Neyman-Pearson Lemma (see earlier entry on this) lets us walk through the list in the following fashion: We generate the simulated data sets under model A and compare likelihood ratios in each case. If the likelihood ratio of the observed data falls outside the 95% confidence interval, than it with this confidence that we are saying the data justify the alternate model (model B). Then repeat, generating under model B and comparing to model C, and so forth until we cannot reject the simpler model.
More subtle concerns:
- The model generating the data used in the bootstrap in an estimate, and shouldn't be treated as the true model with no uncertainty, but rather be bootstrapped itself.
- Of course the method should consider probabilistic partitions of the data. The MLE partition alone will be misleading.
- My lightning talk proposal to iEvoBio was accepted!
- Still mostly working in the Stochastic Population Dynamics notebook this week.