mixedDiffusion-class: S4 class of model informations for hierarchical (mixed)...

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Informations of model dY_t = b(φ_j,t,Y_t)dt + γ \widetilde{s}(t,Y_t)dW_t, φ_j\sim N(μ, Ω), Y_{t_0}=y_0(φ, t_0).

Slots

phi

parameter φ

mu

parameter μ

Omega

parameter Ω

gamma2

parameter γ^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

mu <- c(2, 1); Omega <- c(1, 0.04)
phi <- sapply(1:2, function(i) rnorm(21, mu[i], sqrt(Omega[i])))
parameter <- list(phi = phi, mu = mu, Omega = Omega, gamma2 = 0.01)
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.mu = parameter$mu, v.mu = parameter$mu^2,
   alpha.omega = rep(3, length(parameter$mu)), beta.omega = parameter$Omega*2,
   alpha.gamma = 3, beta.gamma = parameter$gamma2*2)
model <- set.to.class("mixedDiffusion", 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.