Goodness of Fit

Goodness-of-fit testing assesses how well a statistical model represents observed data, aiming to determine if the data plausibly originated from the proposed model or a similar, mildly perturbed one. Current research emphasizes robust methods, particularly those using kernel-based techniques like the Kernelized Stein Discrepancy (KSD) and Maximum Mean Discrepancy (MMD), to address the limitations of traditional approaches and handle complex models, including those with unnormalized densities or high dimensionality. These advancements are crucial for various applications, from validating probabilistic models in diverse fields to improving the reliability of machine learning algorithms like generative adversarial networks and clustering methods.