Statistical Analysis and AI Models in the Big Data Era

Dear Colleagues,

In the current era of vast amounts of data, statistical analysis has evolved from small-sample inference to addressing high-dimensional, streaming, and diverse data where traditional assumptions often do not apply. Modern AI models, including deep neural networks and large generative systems, act as powerful, non-parametric function approximators. However, their vast capacity brings new statistical issues such as uncertainty quantification, robustness to distributional changes, and interpretability across billions of parameters.

Today’s statistical methods are closely integrated with algorithm development: Bayesian deep learning offers calibrated uncertainty estimates through variational inference and Monte Carlo dropout with post-specific experimental settings to recalibrate the outputs; conformal prediction ensures finite-sample coverage without relying on distributional assumptions; and robust statistics defend against adversarial or corrupted data. Simultaneously, causal inference and counterfactual reasoning are being adapted for large observational datasets, supporting policy decisions in medicine, finance, and climate science.

In essence, the integration of rigorous statistical theory and scalable AI architectures is propelling a transition from basic prediction to principled, reliable decision-making under uncertainty in the Big Data era.

We eagerly anticipate contributions that advance our understanding in these critical domains.

Dr. Achraf Cohen
Prof. Dr. Yichuan Zhao
Guest Editors

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