estimate-Diffusion-method: Estimation for diffusion process

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

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.

Usage

## S4 method for signature 'Diffusion'
estimate(model.class, t, data, nMCMC, propSd,
  adapt = TRUE, proposal = c("normal", "lognormal"))

Arguments

model.class	class of the diffusion process model including all required information, see Diffusion-class
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)

References

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.

Examples

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)

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.