Results 61 to 70 of about 7,662,414 (297)
Frontiers in Marine Science, 2016 We present a Hierarchical Bayesian version of Pollock's Closed Robust Design for studying the survival, temporary-migration, and abundance of marked animals.
Robert William Rankin +5 more
doaj +1 more source
A normative model for Bayesian combination of subjective probability estimates
Judgment and Decision Making, 2023 Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally.
Susanne Trick +2 more
doaj +1 more source
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 +3 more
core +1 more source
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 +8 more
wiley +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 +3 more
wiley +1 more source
Hierarchical Bayesian autoregressive smooth transition time series models
Frontiers in Applied Mathematics and StatisticsBackgroundPublic 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 +1 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 +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 +24 more
wiley +1 more source
Journal of MathematicsBoth 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 +2 more
doaj +1 more source