Grammatical inference seeks to deduce formal language rules from observed sequences of symbols. Traditional methods often rely on exhaustive search or heuristic grammar transformations, which can ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...

In the quest to unravel the underlying mechanisms of natural systems, accurately identifying causal interactions is of paramount importance. Leveraging the advancements in time-series data collection ...

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning ...

While econometrics and statistics drove modern quasi-experimental methods for causal inference in policy evaluation, public ...

Given the high costs and slow speed of training large language models (LLMs), there is an ongoing discussion about whether spending more compute cycles on inference can help improve the performance of ...

This course provides a rigorous yet practical introduction to Bayesian methods for statistical inference, probabilistic modeling, knowledge representation, and decision-making under uncertainty.

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I ...

Geophysical data offer incomplete clues about the subsurface. Bayesian inversion turns them into plausible scenarios and ...