Large language models (LLMs) are rapidly being integrated into clinical workflows, supporting tasks such as diagnosis ...
Objective Unlike several other fields of healthcare, little is known about the size of ‘therapist effects’ on patient ...
This is achieved via Bayesian Design of Experiments, which helps to efficiently navigate parameter search spaces. It balances exploitation of parameter space regions known to lead to good outcomes and ...
Introduction There is a worldwide need to enhance the capacity of audiometry testing. The objective of this study is to compare the User-operated Audiometry (UAud) system with traditional audiometry ...
Abstract: Hybrid deteriorating systems, which are made up of both linear and nonlinear degradation parts, are often encountered in engineering practice, such as gyroscopes which are frequently ...
As rare disease trials face persistent feasibility challenges, Bayesian designs are gaining momentum by enabling more flexible, data-driven approaches that integrate prior knowledge, reduce sample ...
Abstract: Remaining useful life prediction for degrading systems plays a key role in prevalent prognostics and health management discipline. Compared with the offline remaining useful life prediction ...
Flat prior (not usually recommended); Super-vague but proper prior: normal(0, 1e6) (not usually recommended); Weakly informative prior, very weak: normal(0, 10); Generic weakly informative prior: ...
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ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
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