Search: Bayesian inference — Academic Articles

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses

Audrey Béliveau, Devon J. Boyne, Justin Slater, Darren Brenner et al. • BMC Medical Research Methodology (2019)

Abstract Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not re...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12874-019-0829-2

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SA-M-SBL: An Algorithm for CSI-Based Device-Free Localization With Faulty Prior Information

Dongping Yu, Yan Guo, Ning Li, Meng Wang • IEEE Access (2019)

As an emerging technique, device-free localization (DFL) is promising to localize the target without attaching any transceivers. Recently, the benefits of channel state information (CSI) on DFL have been revealed in this paper. Motivated by...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2019.2916194

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Interplay Between Vertical and Horizontal Schemes of Computation: From Bayesian Inference to Quantum Logic via Gluing Boolean Algebras

Yukio-Pegio Gunji, Kyoko Nakamura, Kazuto Sasai, Iori Tani et al. • Entropy (2026)

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are mate...

DOAJ Open Access (DOAJ) DOI: 10.3390/e28050498

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A recurrent neural network framework for flexible and adaptive decision making based on sequence learning.

Zhewei Zhang, Huzi Cheng, Tianming Yang • PLoS Computational Biology (2020)

The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. Th...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pcbi.1008342

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Machine Learning for Detection and Diagnosis of Disease

Paul Sajda • Annual Review of Biomedical Engineering (2006)

Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. This review focuses on several advances in the state of the art...

OPENALEX DOI: https://doi.org/10.1146/annurev.bioeng.8.061505.095802

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Machine Learning: A Bayesian and Optimization Perspective

Sergios Theodoridis (2015)

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabil...

OPENALEX

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Academic Articles & Research Papers

BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses

SA-M-SBL: An Algorithm for CSI-Based Device-Free Localization With Faulty Prior Information

Interplay Between Vertical and Horizontal Schemes of Computation: From Bayesian Inference to Quantum Logic via Gluing Boolean Algebras

A recurrent neural network framework for flexible and adaptive decision making based on sequence learning.

Machine Learning for Detection and Diagnosis of Disease

Machine Learning: A Bayesian and Optimization Perspective