dpinar: DP-INAR model training

Description Usage Arguments Value

View source: R/dpinar.R

Description

Computes the posterior distribution for the DP-INAR family using a Gibbs sampler.

Usage

1
2
3
dpinar(time_series, p = 1, prior = list(a_alpha = NULL, a_tau = NULL,
  b_tau = NULL, a0 = NULL, b0 = NULL, lambda_max = NULL), burn_in = 10^3,
  chain_length = 10^4, random_seed = 1761, verbose = TRUE)

Arguments

time_series

A univariate time series.

prior

List of prior hyperparameters where:

a_alpha

Hyperparameters of the thinning component.

a_tau, b_tau

Hyperparameters of the concentration parameter with Gamma prior.

a0, b0

Base measure hyperparameters.

lambda_max

Hyperparameter of the continuous uniform distribution that minimizes the corresponding D-KL.

burn_in

Number of iterations for the "burn-in" period which are discarded in the chain.

chain_length

Number of iterations of the chain.

random_seed

Value of the random seed generator.

verbose

If TRUE log info is provided.

Value

dpinar returns an object of class "dpinar".


bayesianfactory/BayesINAR documentation built on Dec. 16, 2019, 12:38 a.m.