ABSTRACT: This work introduces a novel Bayesian inspired regression method for the simultaneous estimation of model parameters and data uncertainties. The key mathematical result of this framework is ...
Complete [Forward Uncertainty Quantification](forward_uq.qmd) before this tutorial. ## Flipping the Arrow The [forward UQ tutorial](forward_uq.qmd) solved this ...
This valuable study uses naturalistic movie-viewing fMRI and stacked encoding models to investigate sensory feature representations in autistic and non-autistic youth, showing a relative shift toward ...
Abstract: Bayesian optimization is a sequential optimization method that is particularly well suited for problems with limited computational budgets involving expensive and non-convex black-box ...
Abstract: Uncertainty quantification is an important task in machine learning - a task in which standard neural networks (NNs) have traditionally not excelled. This can be a limitation for ...
The use of statistics is ubiquitous in astronomy and astrophysics. Modern advances are made possible by the application of increasingly sophisticated tools, often dubbed "data mining", "machine ...
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