In cboettig/nonparametric-bayes: Nonparametric Bayes inference for ecological models

opts_chunk$set(external = TRUE, cache = FALSE, cache.path = "myers-cache/")
read_chunk('gaussian-process-control.R')
library(knitcitations)

We use the Ricker model

sigma_g <- 0.05
z_g <- function(sigma_g) rlnorm(1, 0, sigma_g) #1+(2*runif(1, 0,  1)-1)*sigma_g #
x_grid <- seq(0, 2.5 * K, length=101)
h_grid <- x_grid
profit = function(x,h) pmin(x, h)
delta <- 0.01
OptTime = 20
reward = profit(x_grid[length(x_grid)], x_grid[length(x_grid)]) + 1 / (1 - delta) ^ OptTime 
xT <- 0

with parameters r p.

x_0_observed <- K
xT <- 0
set.seed(1)

We simulate data under this model, starting from a size of r x_0_observed.

We consider the observations as ordered pairs of observations of current stock size $x_t$ and observed stock in the following year, $x_{t+1}$. We add the pseudo-observation of $0,0$. Alternatively we could condition strictly on solutions passing through the origin, though in practice the weaker assumption is often sufficient.

Estimate data under the BH model

We fit a Gaussian process with

The transition matrix of the inferred process

bibliography("html")

cboettig/nonparametric-bayes documentation built on May 13, 2019, 2:09 p.m.