WAIC stands for the widely applicable information criterion (or Watanabe-Akaike information criterion). It is used for model selection, particularly in Bayesian settings.
I am trying to figure out how to interpret the WAIC value computed based on two different Bayesian models. Is the value only used for comparing the models, such that the predictive capabilities of ...
My question is does anyone know of a formal way to test the difference between two models using WAIC and effective degrees of freedom? Any help much appreciated.
bayesian - Testing difference between two models using WAIC and degrees ...
1 How to compare WAIC values when they are negative? Is it still the lower the better? I got two complex Gaussian models with continuous probability density and the WAIC value are -8351 and -7321, respectively. Is the model with lower WAIC better? Thank you!
I would like to do model selection using WAIC, rather than DIC. The JagsUI package does not provide a method to extract WAIC from a bayesian model so I am trying to use the rjags function jags.samples () to extract the WAIC from my models; however, I am only able to accomplish this when my JagsUI model is run without using parallel.
I wanted to calculate WAIC as I have heard it is more robust for hierarchical models. Below is a simplified version of my JAGS code, I have 3 continuous response variables, Dim.1, Dim.2 and Dim.3, and use partial pooling to allow the coefficients to vary by the Site in which they were measured.
How to calculate WAIC from a JAGS model, and fix p_waic issue?