C&EN

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. In this paper, we introduce a methodology to improve upon the ...
Frontiers

A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools

1 Minutia.AI Pte. Ltd., Singapore, Singapore 2 Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy A representation of the cause-effect mechanism is ...
Frontiers

Bayesian estimation of orientation and direction tuning captures parameter uncertainty

1 Department of Biochemistry, Brandeis University, Waltham, MA, United States 2 Department of Biology, Brandeis University, Waltham, MA, United States This study explores the efficacy of Bayesian ...
IEEE

Bayesian Posterior Distribution Estimation of Kinetic Parameters in Dynamic Brain PET Using Generative Deep Learning Models

Abstract: Positron Emission Tomography (PET) is a valuable imaging method for studying molecular-level processes in the body, such as hyperphosphorylated tau (ptau) protein aggregates, a hallmark of ...
IEEE

Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals

Abstract: This work studies the problem of jointly estimating unknown parameters from Kronecker-structured multidimensional signals, which arises in applications like intelligent reflecting surface ...
Scientific Research Publishing

Hódar, J.A. (1996) The Use of Regression Equations for Estimation of Arthropod Biomass in Ecological Studies. Acta Oecologica, 17, 421-433.

ABSTRACT: Exotic bush honeysuckles (Lonicera spp.) are becoming increasingly common in the eastern and mid-western United States, but little is known about their impacts on invertebrates. We used a ...
PNAS

Learning to estimate sample-specific transcriptional networks for 7,000 tumors

Network estimation is essential for understanding the structure and function of biological systems, but current statistical approaches fail to capture intersubject heterogeneity or cross-modality ...
GitHub

Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing

Bayesian network classifiers (BNCs) possess a number of properties desirable for a modern classifier: They are easily interpretable, highly scalable, and offer adaptable complexity. However, ...