bdp_dw_bayes_factor_k1

Estimating the Bayes factor of hypothesis "k1 = 1".

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Renate Meyer

beyondWhittle

Bayesian Spectral Inference for Time Series

Alexander Meier

Claudia Kirch

Matthew C. Edwards

Yifu Tang

bdp_dw_bayes_factor_k1 function

<dl><dt>post_sample</dt>
<dd>the posterior sample generated by bdp_dw_mcmc.</dd>
<dt>precision</dt>
<dd>a positive integer specifying the number of terms used in approximating
the normalizing constant of the prior probability mass function of k1. Default 1000.</dd></dl>

Arguments

Estimating the Bayes factor of hypothesis "k1 = 1". — bdp_dw_bayes_factor_k1

<dl>

<dt>post_sample</dt>
<dd>the posterior sample generated by bdp_dw_mcmc.</dd>


<dt>precision</dt>
<dd>a positive integer specifying the number of terms used in approximating
the normalizing constant of the prior probability mass function of k1. Default 1000.</dd>

</dl>

bdp_dw_bayes_factor_k1: Estimating the Bayes factor of hypothesis "k1 = 1".

Description

Usage

Value

Arguments

References