Description Usage Arguments References Examples
Bayesian estimation of the parameters in the hierarchical 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) with the particle Gibbs sampler.
1 2 3 |
model.class |
class of the hierarchical hidden diffusion model including all required information, see |
t |
list or vector of time points |
data |
list or matrix of observation variables |
nMCMC |
length of Markov chain |
propSd |
vector of proposal variances for φ |
adapt |
if TRUE (default), proposal variance is adapted |
proposal |
proposal density: "normal" (default) or "lognormal" (for positive parameters) |
Npart |
number of particles in the particle Gibbs sampler |
Andrieu, C., A. Doucet and R. Holenstein (2010). Particle Markov Chain Monte Carlo Methods. Journal of the Royal Statistical Society B 72, pp. 269-342.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | mu <- c(5, 1); Omega <- c(0.9, 0.04)
phi <- cbind(rnorm(21, mu[1], sqrt(Omega[1])), rnorm(21, mu[2], sqrt(Omega[2])))
y0.fun <- function(phi, t) phi[2]
model <- set.to.class("hiddenmixedDiffusion", y0.fun = y0.fun,
b.fun = function(phi, t, y) phi[1],
parameter = list(phi = phi, mu = mu, Omega = Omega, gamma2 = 1, sigma2 = 0.01))
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, plot.series = TRUE)
## Not run:
est <- estimate(model, t, data$Z[1:20,], 2000)
plot(est)
## End(Not run)
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