Results 61 to 70 of about 7,749,372 (301)

Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models [PDF]

, 2011
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian ...
Danks, David, Frederick Eberhardt, David Danks, Eberhardt, Frederick   +3 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

Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models

Heliyon
One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy for each area separately is unrealistic due to limitation of resources as well as having issues of ignoring dependencies ...
Irving Gómez-Méndez, Chainarong Amornbunchornvej   +1 more
doaj   +1 more source

Hierarchical Bayesian Models of Subtask Learning

Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task,
Jeromy, Anglim, Sarah K A, Wynton
openaire   +3 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
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, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Bayesian hierarchical models for disease mapping applied to contagious pathologies.

PLoS ONE, 2021
Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose
Sylvain Coly, Myriam Garrido, David Abrial, Anne-Françoise Yao   +3 more
doaj   +1 more source

Classification of chirp signals using hierarchical bayesian learning and MCMC methods [PDF]

, 2002
This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods.
Davy, Manuel, Doncarli, Christian, Jean-yves Tourneret, Tourneret, Jean-Yves, Christian Doncarli, Manuel Davy   +5 more
core   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

The Best Fit Bayesian Hierarchical Generalized Linear Model Selection Using Information Complexity Criteria in the MCMC Approach

Journal of Mathematics
Both frequentist and Bayesian statistics schools have improved statistical tools and model choices for the collected data or measurements. Model selection approaches have advanced due to the difficulty of comparing complicated hierarchical models in ...
Endris Assen Ebrahim, Mehmet Ali Cengiz, Erol Terzi   +2 more
doaj   +1 more source

Hierarchical Bayesian autoregressive smooth transition time series models

Frontiers in Applied Mathematics and Statistics
BackgroundPublic health policy and disease surveillance systems require accurate forecasting of infectious disease dynamics to support timely interventions and resource allocation. However, classical linear time-series models often fail to capture abrupt
Geoffrey Chiyuzga Singini, Samuel O. M. Manda   +1 more
doaj   +1 more source