estimate,Diffusion-method {BaPreStoPro} | R Documentation |
Bayesian estimation of the parameters φ and γ^2 of the stochastic process dY_t = b(φ,t,Y_t)dt + γ \widetilde{s}(t,Y_t)dW_t.
## S4 method for signature 'Diffusion' estimate(model.class, t, data, nMCMC, propSd, adapt = TRUE, proposal = c("normal", "lognormal"))
model.class |
class of the diffusion process 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) |
Hermann, S., K. Ickstadt and C. H. Mueller (2016). Bayesian Prediction of Crack Growth Based on a Hierarchical Diffusion Model. Applied Stochastic Models in Business and Industry, DOI: 10.1002/asmb.2175.
model <- set.to.class("Diffusion", parameter = list(phi = 0.5, gamma2 = 0.01)) t <- seq(0, 1, by = 0.01) data <- simulate(model, t = t, y0 = 0.5, plot.series = TRUE) est_diff <- estimate(model, t, data, 1000) plot(est_diff)