estimate {BaPreStoPro} | R Documentation |
Estimation method for the S4 classes.
estimate(model.class, t, data, nMCMC, propSd, adapt = TRUE, proposal = c("normal", "lognormal"), ...)
model.class |
class object with model informations, see |
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 |
class object est.
model.class
containing Markov chains, data input and model informations
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.