lprior: Log prior of Bernstein-Dirichlet mixture and parametric...
In beyondWhittle: Bayesian Spectral Inference for Time Series
View source: R/gibbs_np_help.R
lprior R Documentation
Log prior of Bernstein-Dirichlet mixture and parametric working model – all unnormalized
Description
Log prior of Bernstein-Dirichlet mixture and parametric working model – all unnormalized
Usage
lprior(
v,
w,
k,
tau,
M,
g0.alpha,
g0.beta,
k.theta,
tau.alpha,
tau.beta,
f.alpha,
corrected,
rho,
rho.alpha,
rho.beta
)
Details
See Section 3 in Kirch et al (2018).
Hyperparameters are M, g0.a, g0.b, k.theta, tau.alpha, tau.beta.
Note: Flat prior on f.alpha.
beyondWhittle documentation built on June 22, 2024, 11:35 a.m.
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.
View source: R/gibbs_np_help.R
| lprior | R Documentation |
Log prior of Bernstein-Dirichlet mixture and parametric working model – all unnormalized
Description
Log prior of Bernstein-Dirichlet mixture and parametric working model – all unnormalized
Usage
lprior(
v,
w,
k,
tau,
M,
g0.alpha,
g0.beta,
k.theta,
tau.alpha,
tau.beta,
f.alpha,
corrected,
rho,
rho.alpha,
rho.beta
)
Details
See Section 3 in Kirch et al (2018). Hyperparameters are M, g0.a, g0.b, k.theta, tau.alpha, tau.beta. Note: Flat prior on f.alpha.
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.