estimate: Bayesian estimation

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Estimation method for the S4 classes.

Usage

estimate(model.class, t, data, nMCMC, propSd, adapt = TRUE,
  proposal = c("normal", "lognormal"), ...)

Arguments

model.class	class object with model informations, see set.to.class
t	vector or list of time points
data	vector or list or matrix of observation variables
nMCMC	length of Markov chain
propSd	vector of proposal variances
adapt	if TRUE (default), proposal variance is adapted
proposal	proposal density: "normal" (default) or "lognormal" (for positive parameters)
...	parameters dependent on the model class

Value

class object est.model.class containing Markov chains, data input and model informations

References

Hermann, S. (2016). BaPreStoPro: an R Package for Bayesian Prediction of Stochastic Processes. SFB 823 discussion paper 28/16.

Robert, C. P. and G. Casella (2004). Monte Carlo Statistical Methods. Springer, New York.

Rosenthal, J. S. (2011). Optimal Proposal Distributions and Adaptive MCMC. In: Handbook of Markov Chain Monte Carlo, pp. 93-112.

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