Description Usage Arguments References Examples
Bayesian estimation of the 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) with a particle Gibbs sampler.
1 2 3 |
model.class |
class of the hidden diffusion model including all required information, see |
t |
vector of time points |
data |
vector 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 15 16 17 18 19 | model <- set.to.class("hiddenDiffusion", y0.fun = function(phi, t) 0.5,
parameter = list(phi = 5, gamma2 = 1, sigma2 = 0.1))
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, plot.series = TRUE)
est <- estimate(model, t, data$Z, 100) # nMCMC should be much larger!
plot(est)
## Not run:
# OU
b.fun <- function(phi, t, y) phi[1]-phi[2]*y
model <- set.to.class("hiddenDiffusion", y0.fun = function(phi, t) 0.5,
parameter = list(phi = c(10, 1), gamma2 = 1, sigma2 = 0.1),
b.fun = b.fun, sT.fun = function(t, x) 1)
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, plot.series = TRUE)
est <- estimate(model, t, data$Z, 1000)
plot(est)
## End(Not run)
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