hiddenDiffusion-class: S4 class of model informations for hidden diffusion process

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Informations of model Z_i = Y_{t_i} + ε_i, dY_t = b(φ,t,Y_t)dt + γ \widetilde{s}(t,Y_t)dW_t, ε_i\sim N(0,σ^2), Y_{t_0}=y_0(φ, t_0).

Slots

phi

parameter φ

gamma2

parameter γ^2

sigma2

parameter σ^2

y0.fun

function y_0(φ, t)

b.fun

function b(φ,t,y)

sT.fun

function \widetilde{s}(t,y)

prior

list of prior parameters

start

list of starting values for the Metropolis within Gibbs sampler

Examples

parameter <- list(phi = c(2, 1), gamma2 = 0.1, sigma2 = 0.1)
b.fun <- function(phi, t, y) phi[1] * y
sT.fun <- function(t, y) y
y0.fun <- function(phi, t) phi[2]
start <- parameter
prior <- list(m.phi = parameter$phi, v.phi = parameter$phi^2, alpha.gamma = 3,
   beta.gamma = parameter$gamma2*2, alpha.sigma=3, beta.sigma=parameter$sigma2*2)
model <- set.to.class("hiddenDiffusion", parameter, prior, start,
  b.fun = b.fun, sT.fun = sT.fun, y0.fun = y0.fun)

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.