Genome-wide association studies (GWAS) have catalogued hundreds of thousands of genetic variants linked to complex human traits and diseases, with more than 625,000 variant-trait associations across ...
Bayesian methods are becoming an increasingly popular approach to data analysis across a wide range of research fields. They offer a flexible and structured framework for statistical inference, ...
Abstract: This article introduces the Bayesian probabilistic fuzzy neural network (BPFNN), a unified architecture designed to overcome the challenges of conventional fuzzy clustering and neural ...
Abstract: Error entropy is a potent tool for quantifying the similarity between two random vectors, occupying a significant position in state estimation. However, the ...
2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China Objective To demonstrate an application of Bayesian model averaging (BMA) with generalised additive ...
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of ...
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This project applies Bayesian statistical methods to model stock returns and volatility for five Indian equities. Using historical market data, the project estimates posterior distributions of ...
As additional reference, we also calculated a classical (non-Bayesian) logistic regression model. The transition from a linear model in the simulation to a logistic regression model in the ...
Gu et al., 2025 ). In contrast, the Bayesian neural network adopted in this study showed stronger adaptability and robustness in multimodal and multi-parameter prediction tasks. Its core advantage ...
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