Informations of model Z_{ij} = Y_{t_{ij}} + ε_{ij}, 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), ε_{ij}\sim N(0,σ^2).
phi
parameter φ
mu
parameter μ
Omega
parameter Ω
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
1 2 3 4 5 6 7 8 9 10 11 12 13 | 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 = 0.1, 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.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$gamma2*2,
alpha.sigma = 3, beta.sigma = parameter$sigma2*2)
model <- set.to.class("hiddenmixedDiffusion", parameter, prior, start,
b.fun = b.fun, sT.fun = sT.fun, y0.fun = y0.fun)
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