mcmc.gesm: MCMC algorithm for Generalized Elliptical Semi-parametric...
In BayesGESM: Bayesian Analysis of Generalized Elliptical Semi-Parametric Models and Flexible Measurement Error Models
Description
Usage
Arguments
Author(s)
Description
This function implements the MCMC algorithm derived in order to draw samples of
the posterior distribution of the interest parameters in generalized elliptical semi-parametric models,
which combines Gibbs sampler and Metropolis-Hastings algorithm.
Usage
1
mcmc.gesm(params)
Arguments
params
An object type list, which provides the setup (i.e., values of hyperparameters, initial values, model matrices, basis functions for
the B-splines, burn-in and posterior sample size) of the model requested by the user.
Author(s)
Luz Marina Rondon <lumarp@gmail.com> and Heleno Bolfarine
BayesGESM documentation built on May 2, 2019, 11:27 a.m.
R Package Documentation
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Description Usage Arguments Author(s)
Description
This function implements the MCMC algorithm derived in order to draw samples of the posterior distribution of the interest parameters in generalized elliptical semi-parametric models, which combines Gibbs sampler and Metropolis-Hastings algorithm.
Usage
1 | mcmc.gesm(params)
|
Arguments
params |
An object type list, which provides the setup (i.e., values of hyperparameters, initial values, model matrices, basis functions for the B-splines, burn-in and posterior sample size) of the model requested by the user. |
Author(s)
Luz Marina Rondon <lumarp@gmail.com> and Heleno Bolfarine
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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