Results 111 to 120 of about 11,544,586 (297)
Hierarchical Bayesian sparse image reconstruction with application to MRFM [PDF]
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally
Hero, Alfred O. +2 more
core +1 more source
In a research study, population data are often not available, so the population parameter is unknown. Meanwhile, knowledge about the population parameter is needed to know the characteristics of the studied population. Therefore, it is needed to estimate
Muthia Nadhira Faladiba, Atina Ahdika
doaj +1 more source
Bayesian estimation of the measurement of interactions in epidemiological studies [PDF]
Background Interaction identification is important in epidemiological studies and can be detected by including a product term in the model. However, as Rothman noted, a product term in exponential models may be regarded as multiplicative rather than ...
Shaowei Lin +3 more
doaj +2 more sources
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 +8 more
wiley +1 more source
In recent years, advances in analyses of the sperm morphology and genetics of Perumytilus purpuratus have allowed to two evolutionary scenarios for this mussel to be suggested: (1) the scenario of cryptic species and (2) the scenario of incipient or in ...
Carolina Briones +8 more
doaj +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
Honorary Lecture on S. James Press and Bayesian Analysis [PDF]
S. James Press's many contributions to statistical research, lecturing, mentoring students, the statistics profession, etc. are summarized. Then some new developments in Bayesian analysis are described and remarks on the future of Bayesian analysis are ...
Arnold Zellner
core
The mechanical stratification of soils based on the cone penetration test data (such as soil classification index Ic) is widely applied. However, the soil stratification based on Ic depends on engineering experience, and the subjective uncertainty is ...
CAO Zijun 1 , HU Chao 2, WANG Yafei 3, MIAO Cong 4, , LIU Tao 5, HONG Yi 6, ZHENG Shuo 7
doaj +1 more source
Artificial Intelligence Meets Micro/Nanorobotics
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever +6 more
wiley +1 more source

