Abstract: Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations ...

The behavior of language models is influenced by the prior context provided in prompts. Depending on whether you pick synthesis or shake, the next row looks very different — Vishal Misra Contextual ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG), magnetoencephalography (MEG), and also from invasive ones such as ...

ABSTRACT: A sparse vector regression model is developed. The model is established by employing Bayesian formulation and trained by using a set of data . The parameters needed to be determined in the ...

ABSTRACT: This paper presents a novel variable selection method in additive nonparametric regression model. This work is motivated by the need to select the number of nonparametric components and ...

System identification learns models of dynamical systems from input–output measurements. Estimated models should generalize by predicting system’s output responses to new, previously unseen inputs.

Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing ...

Abstract: In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where ...