Firth penalization reduces small-sample bias and produces finite estimates even when standard MLE fails due to (quasi-)complete separation or monotone likelihood. Standard maximum-likelihood logistic ...

Himalaya [1] implements machine learning linear models in Python, focusing on computational efficiency for large numbers of targets. Use himalaya if you need a library that: estimates linear models on ...

The right Python libraries can dramatically improve speed, efficiency, and maintainability in 2025 projects. Mastering a mix of data, AI, and web-focused libraries ensures adaptability across multiple ...

Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan Advanced Research Laboratory, Technology Infrastructure Center, ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

Scientists have long sought to derive models from extensive observational input–output data, ensuring these models accurately capture the underlying mapping from inputs to outputs while remaining ...

Geothermal heat flow (GHF) data measured directly from boreholes are sparse. Purely physics-based models for geothermal heat flow prediction require various simplifications and are feasible only for ...

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic ...

Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom Laboratory of Computational Science and Modeling, IMX, ...