Results 51 to 60 of about 7,749,372 (301)
Alexandria Engineering Journal, 2023 The hierarchical models have not only a major concern with developing computational schemes but also assist in inferring the multi-parameter problems.
Azeem Iqbal +2 more
doaj +1 more source
Hierarchical Bayesian sparse image reconstruction with application to MRFM [PDF]
, 2008 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
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Bayesian Hierarchical Models for Counterfactual Estimation
CoRR, 2023 Counterfactual explanations utilize feature perturbations to analyze the outcome of an original decision and recommend an actionable recourse. We argue that it is beneficial to provide several alternative explanations rather than a single point solution and propose a probabilistic paradigm to estimate a diverse set of counterfactuals.
Natraj Raman +2 more
openaire +4 more sources
Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
wiley +1 more source
Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach
Remote Sensing, 2019 Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar
Mengxi Wang +4 more
doaj +1 more source
Latent diffusion models for event history analysis [PDF]
, 2007 We consider Bayesian hierarchical models for event history analysis, where the event times are modeled through an underlying diffusion process, which determines the hazard rate. We show how these models can be e±ciently treated by means of Markov chain
Roberts, Gareth O., Sangalli, Laura M.
core
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 +3 more
wiley +1 more source
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