some thoughts following a model discussion in Stats 691 with Michael Stein:
the model currently assumes that the initial conditions (starting population sizes in each patch of the three size classes) are known without error. this is definitely not the case; I'd like to explore sensitivity to this assumption, maybe by randomly varying the year 0 inputs to see if model outputs change much.
relatedly, this may be an overlooked source of error in every time step, if the model assumes that the population sizes simulated last year are known without error. right now, really the only source of variability seems to be the initial parameter draws; after that the model is fully deterministic (?). I'm not sure computationally how we'd do this but Michael suggested adding just a little bit of variability in N_A, N_J, and N_Y every year, maybe with Markov Chain models (which I've only used in MCMC, need to look into this).
some thoughts following a model discussion in Stats 691 with Michael Stein:
the model currently assumes that the initial conditions (starting population sizes in each patch of the three size classes) are known without error. this is definitely not the case; I'd like to explore sensitivity to this assumption, maybe by randomly varying the year 0 inputs to see if model outputs change much.
relatedly, this may be an overlooked source of error in every time step, if the model assumes that the population sizes simulated last year are known without error. right now, really the only source of variability seems to be the initial parameter draws; after that the model is fully deterministic (?). I'm not sure computationally how we'd do this but Michael suggested adding just a little bit of variability in N_A, N_J, and N_Y every year, maybe with Markov Chain models (which I've only used in MCMC, need to look into this).