Transfer Learning in Multiple Hypothesis Testing

Transfer Learning in Multiple Hypothesis Testing

In this investigation, a synthesis of Convolutional Neural Networks (CNNs) and Bayesian inference is presented, leading to a novel approach to the problem of Multiple Hypothesis Testing (MHT). Diverging from traditional paradigms, this study introduces a sequence-based uncalibrated Bayes factor appr...

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Bibliographic Details
Main Authors: Stefano Cabras, María Eugenia Castellanos Nueda
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Entropy
Subjects:
bayes factors
deep learning
improper priors
objective bayesian inference
random sequences
RNA-seq experiments
Online Access:https://www.mdpi.com/1099-4300/26/1/49

Internet

https://www.mdpi.com/1099-4300/26/1/49

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