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To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference.

PLoS Biology, 2019
To form a percept of the environment, the brain needs to solve the binding problem-inferring whether signals come from a common cause and are integrated or come from independent causes and are segregated.
Máté Aller, Uta Noppeney
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Exact Inference with Approximate Computation for Differentially Private Data via Perturbations

The Journal of Privacy and Confidentiality, 2022
This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products.
Ruobin Gong
doaj  

Variational Bayesian Sparse Signal Recovery With LSM Prior

IEEE Access, 2017
This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang, Yongxiang Liu, Xiang Li, Guoan Bi   +3 more
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Bayesian Methodologies with pyhf [PDF]

EPJ Web of Conferences
bayesian_pyhf is a Python package that allows for the parallel Bayesian and frequentist evaluation of multi-channel binned statistical models. The Python library pyhf is used to build such models according to the HistFactory framework and already ...
Feickert Matthew, Heinrich Lukas, Horstmann Malin   +2 more
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Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

Frontiers in Genetics, 2019
The importance of expression quantitative trait locus (eQTL) has been emphasized in understanding the genetic basis of cellular activities and complex phenotypes. Mixed models can be employed to effectively identify eQTLs by explaining polygenic effects.
Chaeyoung Lee
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A Bayesian Hyperparameter Inference for Radon-Transformed Image Reconstruction

International Journal of Biomedical Imaging, 2011
We develop a hyperparameter inference method for image reconstruction from Radon transform which often appears in the computed tomography, in the manner of Bayesian inference.
Hayaru Shouno, Madomi Yamasaki, Masato Okada   +2 more
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A Bayesian perspective on observers’ inference of group norms

npj Science of Learning
Inferring group norms is crucial for adapting behaviors in novel situations, but its underlying basis and computational account remain unclear. This study manipulated the prevalence of norm-consistent behaviors (i.e., straight-line movements) to examine ...
Jipeng Duan, Xiuyan Guo, Li Zheng, Jun Yin   +3 more
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Application of Markov chain Monte carlo method in Bayesian statistics

MATEC Web of Conferences, 2016
In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical ...
Zhao Qi
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Bayesian inference for the learning rate in Generalised Bayesian inference

33 pages, 7 figures, 1 Table with 32 pages of appendices including 18 further figures and 4 further ...
Lee, Jeong Eun, Liu, Sitong, Nicholls, Geoff K.   +2 more
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Parameter estimation for allometric trophic network models: A variational Bayesian inverse problem approach

Methods in Ecology and Evolution
Differential equation models are powerful tools for predicting biological systems, capable of projecting far into the future and incorporating data recorded at arbitrary times.
Maria Tirronen, Anna Kuparinen
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