84 papers
This paper applies a probabilistic machine learning framework to analyze Collatz stopping times up to $10^7$, demonstrating that a Bayesian hierarchical Negative Binomial regression outperforms a mechanistic odd-block generator in predictive likelihood while revealing that low-order modular structure significantly drives the observed heterogeneity.
This paper employs an inverse optimization framework on NFL play-by-play data to demonstrate that coaches' historically conservative fourth-down decisions are consistent with optimizing low quantiles of future value, revealing that their risk preferences have become more tolerant over time and vary based on field position.
This paper investigates the projected isotropic normal distribution, deriving new properties and practical approximations for its mean resultant statistic to facilitate phase-based analysis of EEG signals recorded during flash stimulation.
This paper introduces a parsimonious Levy-driven graph supOU process for modeling high-dimensional time series with both short- and long-range dependence, develops a consistent generalized method of moments estimator for it, and validates the approach through simulations and an empirical study of European wind capacity factors.