bayes_factor.varstan: Bayes Factors from Marginal Likelihoods.

In bayesforecast: Bayesian Time Series Modeling with Stan

Description

Compute Bayes factors from marginal likelihoods.

Usage

## S3 method for class 'varstan'
bayes_factor(x1, x2, log = FALSE, ...)

Arguments

x1	A varstan object
x2	Another varstan object based on the same data.
log	A boolean parameter for report the Bayes_factor in log scale. The default value is FALSE.
...	Additional arguments passed to bayes_factor.

Details

The computation of marginal likelihoods based on bridge sampling requires a lot more posterior samples than usual. A good conservative rule of thump is perhaps 10-fold more samples (read: the default of 4000 samples may not be enough in many cases). If not enough posterior samples are provided, the bridge sampling algorithm tends to be unstable leading to considerably different results each time it is run. We thus recommend running bridge_sampler multiple times to check the stability of the results.

For more details check the bridgesampling package.

Value

The bayes factors of two models.

Examples

 library(astsa)
 # Fitting a seasonal arima model
 mod1 = Sarima(birth,order = c(0,1,2),seasonal = c(1,1,1))
 fit1 = varstan(mod1,iter = 500,chains = 1)

 # Fitting a Dynamic harmonic regression
 mod2  = Sarima(birth,order = c(0,1,2),xreg = fourier(birth,K=6))
 fit2 = varstan(mod2,iter = 500,chains = 1)

 # compute the Bayes factor
 bayes_factor(fit1, fit2)

bayesforecast documentation built on June 17, 2021, 5:14 p.m.