# User:Carl Boettiger/Notebook/Stochastic Population Dynamics/2010/05/04

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## Alan Meeting- Goals next week: Bob & Brian beetle data: what an analysis would be with complete stochastic description.
- Compare models with different
Full model: Parameter list from current code: /* Biological Parameters. eggs and pupa can get canibalized */ double b = 5; /* Birth rate (per day) */ double u_egg = 0, u_larva = 0.001, u_pupa = 0, u_adult = 0.003; /* Mortality rate (per day) */ double a_egg = 3.8, a_larva = 3.8+16.4, a_pupa = 3.8+16.4+5.0; /* age at which each stage matures, in days */ double cannibal_larva_eggs = 0.01, cannibal_adults_pupa = 0.004, cannibal_adults_eggs = 0.01; /* cannibalism of x on y, per day */ double a_larva_asym = 3.8+8.0; /* age after which larval size asymptotes */ - Add
**individual heterogeneity**in egg and larva maturation age and in cannibalism of larva on eggs.
## Outline / manuscript draft## Stochastic Population Dynamics## Likelihood inference for time series- All based on step-ahead predictions, from Markov property. Compare to deterministic skeleton's minimization of sum of squares on step-ahead predictions (i.e. assumes normal deviates).
See the monographs: - Iacus, S. M. (2008). Simulation and Inference for Stochastic Differential Equations With R Examples. New York: Springer.
- Prakasa Rao, B.L.S. (1999) Statistical Inferences for Diffusion Type Processes, Oxford University Press, New York.
Exploring existing implementations of likelihood methods on SDEs through the R sde package accompanying the Iacus text. - Many nice methods for SDEs, general case is harder.
## Conditions-
*Large sample scheme*Time interval gets longer with*n*, while Δ is fixed time-step. Requires the additional assumptions of stationarity and/or ergodicity -
*High-frequency scheme*: Δ shrinks as n increases, fixed window, need not assume ergodic. -
*Rapidly increasing design*: hybrid combination with prescribed rate of mesh increase*k*
## Underlying model
## Exact Likelihood conditions- Linear growth assumption:
- Global Lipshitz assumption:
| - Positive diffusion coefficient
- Bounded moments
- Smooth coefficients (will use up to 3 times differentiable)
Convergence of diffusion part estimator usually , with ## Numerical methods- Exact likelihood inference (conditional density of process must be known)
- Euler approximation: discretization can assume linearity over small Δt
- Elerian method (Milstein scheme)
- Kessler (higher order Ito/Taylor expansion)
- Simulated likelihood (approximate cdf with subdivisions in timestep over which Euler is accurate).
- Hermite polynomial expansion of likelihood.
## Steps- Evaluate the conditional density function
- Evaluate the likelihood function (will be used as single step predictor)
- maximum likelihood estimation
## Code updates- Swapped out my original linked list library for a more intelligent one. Not sure why pointer pointers are so useful but valgrind is happy.
- beetle simulator now creates step-ahead realizations.
- Considerations: replicates in C or R? would be better if R preserved the openmp code, but can always use parallel R to loop over timesteps and benefit from compiled speed on replicates.
- Kernel density estimation for assigning probabilities? Probably reserve at R level at the moment.
## Misc Reading & Notes |