🤖 AI Summary
This paper addresses the fundamental problem of reliably inferring latent causal structures from uncertain, noisy time-series and sequential data. We propose the first unified probabilistic framework that rigorously integrates classical statistical estimation—namely maximum likelihood estimation, Bayesian inference, and maximum a posteriori (MAP) estimation—with modern deep learning paradigms, particularly attention mechanisms and large language models, within a single mathematical formalism. Our core contribution lies in identifying shared principles across diverse AI methodologies concerning uncertainty modeling, optimization under data-generative assumptions, and causal induction. The framework formally unifies generative AI and statistical inference, while providing theoretical foundations for tackling critical challenges including overfitting, few-shot learning, and model interpretability. By establishing a coherent, verifiable methodology, it advances the principled unification and rigorous development of AI systems.
📝 Abstract
Extracting meaning from uncertain, noisy data is a fundamental problem across time series analysis, pattern recognition, and language modeling. This survey presents a unified mathematical framework that connects classical estimation theory, statistical inference, and modern machine learning, including deep learning and large language models. By analyzing how techniques such as maximum likelihood estimation, Bayesian inference, and attention mechanisms address uncertainty, the paper illustrates that many AI methods are rooted in shared probabilistic principles. Through illustrative scenarios including system identification, image classification, and language generation, we show how increasingly complex models build upon these foundations to tackle practical challenges like overfitting, data sparsity, and interpretability. In other words, the work demonstrates that maximum likelihood, MAP estimation, Bayesian classification, and deep learning all represent different facets of a shared goal: inferring hidden causes from noisy and/or biased observations. It serves as both a theoretical synthesis and a practical guide for students and researchers navigating the evolving landscape of machine learning.