Results 81 to 90 of about 63,287 (274)

Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems

Mathematics, 2019
Many basic laws of physics or chemistry can be written in the form of differential equations. With the development of digital signals and computer technology, the research on discrete models has received more and more attention.
Junxia Ma, Qiuling Fei, Fan Guo, Weili Xiong   +3 more
doaj   +1 more source

Variational Bayesian Inference of Line Spectra [PDF]

IEEE Transactions on Signal Processing, 2017
15 pages, 8 figures, accepted for publication in IEEE Transactions on Signal ...
Mihai-Alin Badiu, Thomas Lundgaard Hansen, Bernard Henri Fleury   +2 more
openaire   +3 more sources

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Performance comparison between maximum likelihood estimation and variational method for estimating simple linear regression parameter [PDF]

ITM Web of Conferences
Variational estimation method is a deterministic approximation technique which involves Bayesian framework while giving a point estimate instead of the usual Bayesian interval estimation. The linear regression model, which has always been a popular model,
Widyaningsih Yekti, Rizka Hakiim Nur, Siswantining Titin   +2 more
doaj   +1 more source

Variational Bayesian Optimal Experimental Design

, 2019
Published as a conference paper at the Thirty-third Conference on Neural Information Processing Systems, Vancouver 2019.
Foster, A, Jankowiak, M, Bingham, E, Horsfall, P, Tee, YW, Rainforth, T, Goodman, N   +6 more
openaire   +4 more sources

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Variational Inference in Nonconjugate Models

, 2013
Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm.
Blei, David M., Wang, Chong
core  

Robust Extended Object Tracking Based on Variational Bayesian for Unmanned Aerial Vehicles Under Unknown Outliers

Drones
The application of extended object tracking (EOT) in unmanned aerial vehicles (UAVs) has increasingly gained attention in recent years. However, EOT is often corrupted by heavy-tailed measurement noise due to outliers, which can be caused by factors such
Haibo Yang, Yu Zhu, Yanning Zhang, Xueling Chen   +3 more
doaj   +1 more source

Variational Bayesian Sparse Signal Recovery With LSM Prior

IEEE Access, 2017
This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang, Yongxiang Liu, Xiang Li, Guoan Bi   +3 more
doaj   +1 more source