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 ...
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 ...
The rapid evolution of autonomous systems is reshaping urban mobility and accelerating the development of intelligent transportation networks. A key challenge in real-world deployment is the ability ...
Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...
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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