Results 71 to 80 of about 1,651,457 (304)

Objective Bayesian calibration and the problem of non-convex evidence [PDF]

, 2012
Jon Williamson's Objective Bayesian Epistemology relies upon a calibration norm to constrain credal probability by both quantitative and qualitative evidence.
Wheeler, Gregory
core  

Bayesian analysis for emerging infectious diseases [PDF]

, 2009
Infectious diseases both within human and animal populations often pose serious health and socioeconomic risks. From a statistical perspective, their prediction is complicated by the fact that no two epidemics are identical due to changing contact habits,
Jewell, Chris, Kypraios, Theodore, Roberts, Gareth, Roberts, Gareth O., Jewell, Chris P., Neal, Peter   +5 more
core   +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

Using the Bayesian method to improve measurement accuracy in conditions of uncertainty and lack of information from information and measurement systems

Известия высших учебных заведений: Проблемы энергетики
RELEVANCE of the research lies in the need to improve the accuracy and reliability of information and measurement systems (AIS) in conditions of uncertainty and incompleteness of information.
L. S. Zvyagin
doaj   +1 more source

Bayesian Solution Uncertainty Quantification for Differential Equations [PDF]

, 2016
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed from a ...
Girolami, MA, Campbell, DA, Calderhead, B, ,, Chkrebtii, OA   +4 more
core   +1 more source

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

Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

Complexity, 2018
Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization.
Gonzalo A. Ruz, Pamela Araya-Díaz
doaj   +1 more source

A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability

Sensors, 2018
Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is ...
Zheng Li, Jingbin Liu, Fan Yang, Xiaoguang Niu, Leilei Li, Zemin Wang, Ruizhi Chen   +6 more
doaj   +1 more source

Bayesian and Consistent Assessments [PDF]

In a Bayesian assessment beliefs are computed from the strategy profile following Bayes’ rule at positive probability information sets and for every subgame.
Carlos Pimienta
core  

Challenges to Bayesian Confirmation Theory [PDF]

, 2011
While Bayesian analysis has enjoyed notable success with many particular problems of inductive inference, it is not the one true and universal logic of induction. I review why the Bayesian approach fails to provide this universal logic of induction. Some
Norton, JD, John D. Norton, Norton, John D.   +2 more
core   +1 more source