2020年7月11日 · The value by itself isn’t interpretable. It could be close to zero, greater than a million, or even negative. It is only useful for comparing models. Note that your models must be fit to the same data though in order to compare WAIC values.

2018年1月25日 · Since you ask about WAIC, it helps if we forget deviance and focus on (negative) log score as in WAIC paper. First to make the terms more clear, p(y|theta) as a function of y is an observation model and p(y|theta) as a function of theta is a likelihood.

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 …

WAIC LOO 1 8408,2 8408,7 2 8408,8 8409,5 3 8407,7 8408,1 4 8408,1 8408,7 5 8407,3 8407,7 6 8407,8 8408,2 7 8407,9 8408,6 I was wondering if there is a rule to determine if the differences are large enough. I haven't found anything online. Clearly this are not the only tools I'm using.

2018年3月6日 · There is more than 1 definition of DIC and WAIC. Celeux et al. (2006) provide 8 variants of DIC; Gelman and Vehtari (2013) provide 2 definitions of WAIC.

2021年2月26日 · Yes there doesn't seem to be a lot of active comparison of AIC to WAIC, so I guess I will just need to quickly re-run all the GLM models using a Bayesian estimator. Not too much of a headache I guess. If you have any official sources or explanation as to why they cannot be compared, would be greatly appreciated. $\endgroup$ –

2023年7月15日 · The Widely Applicable Information Criterion (WAIC, Watanabe, (2010) "Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory". Journal of Machine Learning Research 11 , 3571–3594.), is a fully Bayesian approach for estimating the

2017年9月26日 · WAIC and PSIS-LOO are connected so that if PSIS-LOO fails then WAIC fails even more (Aki Vehtari, Andrew Gelman and Jonah Gabry (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.

WAIC uses the whole posterior density more effectively than DIC does, so Gelman et al. prefer it although it can be a pain to calculate in some cases. Cross-validation does not rely on any particular formula, but it can be computationally prohibitive for many models.

2024年5月28日 · Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization