WAIC, or Widely Applicable Information Criterion, is a measure used for model comparison in Bayesian statistics, focusing on the predictive performance of models. It provides a way to evaluate how well different models can predict new data, balancing model fit and complexity. WAIC is particularly useful because it can be applied to various types of Bayesian models, making it a versatile tool in determining which model best captures the underlying data-generating process.
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