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Discussions highlight appreciation for the article's novel approach, such as mentions of cross-validated Bayesian model selection, with some posts emphasizing its potential to improve analysis reliability. The tone is generally positive and focused on the technical merits, with examples like 'summarize one of your papers' and references to 'new research' that reflect both engagement and recognition of the publication's value.
Most posts acknowledge the importance of the publication, with some explicitly supporting its methods and findings.
Posts indicate a moderate level of curiosity, often referencing the methodology and potential benefits.
Discussion includes references to summarization efforts and the practical implications of the research, reflecting active engagement.
Users recognize the work's significance for improving fMRI analysis and model selection, suggesting notable influence.
I'm starting #PaperVideos: Summarize one of your papers in 5 min or less. Here's an example for our work on Bayesian model selection for general linear models in fMRI data analysis (sorry, it's 5:22 min
#openaccess New research in CRO: September 23, 2019 at 10:57AM How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection https://t.co/dETefG77lb
This video discusses how to improve fMRI data analysis with General Linear Models by using cross-validated Bayesian model selection to choose the best model, enhancing reliability and reproducibility in neuroimaging…
RT @JoramSoch: I'm starting #PaperVideos: Summarize one of your papers in 5 min or less. Here's an example for our work on Bayesian model s…
I'm starting #PaperVideos: Summarize one of your papers in 5 min or less. Here's an example for our work on Bayesian model selection for general linear models in fMRI data analysis (sorry, it's 5:22 min
#openaccess New research in CRO: September 23, 2019 at 10:57AM How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection https://t.co/dETefG77lb
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