Results 131 to 140 of about 4,387 (252)

Testing Hypotheses of Covariate Effects on Topics of Discourse

Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 2, April 2026.
ABSTRACT We introduce an approach to topic modeling with document‐level covariates that remains tractable in the face of large text corpora. This is achieved by de‐emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are
Gabriel Phelan, David A. Campbell
wiley   +1 more source

BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference. [PDF]

Nat Methods
Baele G, Ji X, Hassler GW, McCrone JT, Shao Y, Zhang Z, Holbrook AJ, Lemey P, Drummond AJ, Rambaut A, Suchard MA.   +10 more
europepmc   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

A multimodal fusion model for predicting the roasting-induced crispness of walnut kernels using machine vision, hyperspectral imaging, and electronic nose. [PDF]

Curr Res Food Sci
Fu J, Dai D, Huang S, Xie J, He Y, Dong C, Xia K, Zheng J, Chen S.   +8 more
europepmc   +1 more source

On the Mean‐Field Limit of Consensus‐Based Methods

Mathematical Methods in the Applied Sciences, Volume 49, Issue 5, Page 4214-4240, 30 March 2026.
ABSTRACT Consensus‐based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivative free sampling method referred to as consensus‐based sampling (CBS). In this paper, we investigate the “mean‐field limit” of a class of consensus methods, including
Marvin Koß, Simon Weissmann, Jakob Zech
wiley   +1 more source

Machine Learning Prediction of Laccase‐Catalyzed Oxidation of Aromatic Compounds Using Curated Enzyme‐Specific Datasets

Journal of Computational Chemistry, Volume 47, Issue 7, March 15, 2026.
We curate laccase‐substrate datasets and train five classifiers, from regularized logistic regression to tree‐based models and ChemBERTa, to predict whether a substrate will be oxidized. Feature importance and attention maps projected onto molecular substructures make the predictions interpretable and useful for pre‐screening before the bench ...
Yulia Kulagina, Christian Goldhahn, Ramon Weishaupt, Mark Schubert   +3 more
wiley   +1 more source

Explicit Compression Degradation Estimations for Low-Sampling Single-Pixel Imaging using Hadamard Basis. [PDF]

Adv Sci (Weinh)
Zhang H, Cao J, Zhou C, Yao H, Hao Q.
europepmc   +1 more source

Potential kernels for recurrent Markov chains

Journal of Mathematical Analysis and Applications, 1964
openaire   +1 more source

An Overview of Deep Learning Techniques for Big Data IoT Applications

International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.
Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur, Vipin Balyan, Sindhu Hak Gupta   +2 more
wiley   +1 more source

Marginally Interpretable Spatial Logistic Regression With Bridge Processes. [PDF]

Stat Med
Lee CJ, Dunson DB.
europepmc   +1 more source