Description Usage Arguments References Examples
Bayesian estimation of a stochastic process Y_t = y_0 \exp( φ t - γ^2/2 t+γ W_t + \log(1+θ) N_t).
1 2 3 |
model.class |
class of the jump 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 ξ |
adapt |
if TRUE (default), proposal variance is adapted |
proposal |
proposal density for xi: "normal" (default) or "lognormal" |
it.xi |
number of iterations for MH step for ξ inside the Gibbs sampler |
Hermann, S. and F. Ruggeri (2016). Modelling Wear Degradation in Cylinder Liners. SFB 823 discussion paper 06/16.
Hermann, S., K. Ickstadt and C. H. Mueller (2015). Bayesian Prediction for a Jump Diffusion Process with Application to Crack Growth in Fatigue Experiments. SFB 823 discussion paper 30/15.
1 2 3 4 5 6 7 8 9 10 | model <- set.to.class("Merton", parameter = list(thetaT = 0.1, phi = 0.05, gamma2 = 0.1, xi = 10))
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, y0 = 0.5, plot.series = TRUE)
est <- estimate(model, t, data, 1000)
plot(est)
## Not run:
est_hidden <- estimate(model, t, data$Y, 1000)
plot(est_hidden)
## End(Not run)
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