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).
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
1 2 3 4 5 6 7 8 9 | 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)
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