Log in Sign up
Your paper timeline
Scroll AI takes the way you would scroll a great paper aggregator: quick signal first, deeper critique when something earns your attention, and challenges when a claim feels off.
Trending Newest Top
3 papers in stat.AP
Trending mixes fresh papers with community signal.
0
Identification of physiological shock in intensive care units via Bayesian regime switching models
stat.APstat.MEstat.ML Emmett B. Kendall, Jonathan P. Williams, Curtis B. Storlie et al. · Mar 23, 2026

This paper addresses the critical challenge of detecting occult hemorrhage (internal bleeding) in intensive care units, where delayed diagnosis leads to preventable physiological shock and death. The authors develop a Bayesian regime switching model (RSM) that tracks five latent physiological states—including stable, hemorrhage, and recovery—using longitudinal vital signs (heart rate, MAP, hemoglobin, lactate) and medication history. Applied to 33,924 Mayo Clinic ICU encounters, the model aims to provide interpretable, probabilistic early warnings that outperform standard vital sign monitoring by accounting for autoregressive trends and pre-admission physiological changes.

Detection of occult hemorrhage (i.e., internal bleeding) in patients in intensive care units (ICUs) can pose significant challenges for critical care workers. Because blood loss may not always be clinically apparent, clinicians rely on monitoring vital signs for specific trends indicative of a hemorrhage event. The inherent difficulties of diagnosing such an event can lead to late intervention by clinicians which has catastrophic consequences. Therefore, a methodology for early detection of hemorrhage has wide utility. We develop a Bayesian regime switching model (RSM) that analyzes trends in patients' vitals and labs to provide a probabilistic assessment of the underlying physiological state that a patient is in at any given time. This article is motivated by a comprehensive dataset we curated from Mayo Clinic of 33,924 real ICU patient encounters. Longitudinal response measurements are modeled as a vector autoregressive process conditional on all latent states up to the current time point, and the latent states follow a Markov process. We present a novel Bayesian sampling routine to learn the posterior probability distribution of the latent physiological states, as well as develop an approach to account for pre-ICU-admission physiological changes. A simulation and real case study illustrate the effectiveness of our approach.
AI reviewed
arXivPDF
0
Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
stat.APcs.LG Joanna Zou, Youssef Marzouk · Mar 23, 2026

Training machine learning interatomic potentials (MLIPs) requires costly quantum mechanical calculations to label atomic configurations. This paper proposes using determinantal point processes (DPPs) to select diverse, informative subsets of configurations, mitigating the computational bottleneck while maintaining model accuracy. Experiments on hafnium oxide systems demonstrate that DPP-based subselection achieves competitive or superior performance compared to existing methods like k-means clustering and MaxVol, offering a probabilistic framework that naturally handles variable training set sizes.

The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the task of selecting informative subsets of atomic configurations to label with reference energies and forces from costly quantum mechanical methods. Through experiments with hafnium oxide data, we show that DPPs are competitive with existing approaches to constructing compact but diverse training sets by utilizing kernels of molecular descriptors, leading to improved accuracy and robustness in machine learning representations of molecular systems. Our work identifies promising directions to employ DPPs for unsupervised training data curation with heterogeneous or multimodal data, or in online active learning schemes for iterative data augmentation during molecular dynamics simulation.
AI reviewed
arXivPDF
0
Identifiability and amortized inference limitations in Kuramoto models
stat.APcs.LG Emma Hannula, Jana de Wiljes, Matthew T. Moores et al. · Mar 23, 2026

This paper investigates amortized Bayesian inference (ABI) for estimating coupling parameters in Kuramoto oscillator networks—a nonlinear dynamical system widely used to study synchronization. The authors apply neural posterior estimation via BayesFlow to learn an amortized approximation of the posterior distribution from simulated phase dynamics. While the method succeeds for simple single-parameter networks, the paper's central finding is that it fails for complex multi-node networks due to structural non-identifiability and data inefficiency—making the title's focus on 'limitations' well-earned.

Bayesian inference is a powerful tool for parameter estimation and uncertainty quantification in dynamical systems. However, for nonlinear oscillator networks such as Kuramoto models, widely used to study synchronization phenomena in physics, biology, and engineering, inference is often computationally prohibitive due to high-dimensional state spaces and intractable likelihood functions. We present an amortized Bayesian inference approach that learns a neural approximation of the posterior from simulated phase dynamics, enabling fast, scalable inference without repeated sampling or optimization. Applied to synthetic Kuramoto networks, the method shows promising results in approximating posterior distributions and capturing uncertainty, with computational savings compared to traditional Bayesian techniques. These findings suggest that amortized inference is a practical and flexible framework for uncertainty-aware analysis of oscillator networks.
AI reviewed
arXivPDF