determine_SDP_matrix: Determine the Stochastic Dynamic Programming matrix.

In cboettig/pdg_control: Pretty Darn Good Control

Description

Determine the Stochastic Dynamic Programming matrix.

Usage

determine_SDP_matrix(f, p, x_grid, h_grid, sigma_g, pdfn = function(P, s)
  dlnorm(P, 0, s))

Arguments

f	the growth function of the escapement population (x-h) should be a function of f(t, y, p), with parameters p
p	the parameters of the growth function
x_grid	the discrete values allowed for the population size, x
h_grid	the discrete values of harvest levels to optimize over
sigma_g	the variance of the population growth process
pdfn	the probability density function, taking the proportional chance of a transition to that state, f(x), and the parameter sigma_g By default it will use the log normal density.

Details

this analytical approach doesn't reliably support other sources of variation. The quality of the analytic approximations (lognormal) can be tested.

Value

the transition matrix at each value of h in the grid.

cboettig/pdg_control documentation built on May 13, 2019, 2:10 p.m.