Local Bayesian Dirichlet mixing of imperfect models.

To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

Nurse-Reported Bullying and Documented Adverse Patient Events: An Exploratory Study in a US Hospital.

BACKGROUND: Negative nurse work environments have been associated with nurse bullying and poor nurse health. However, few studies have examined the influence of nurse bullying on actual patient outcomes. PURPOSE: The purpose of the study was to examine the association between nurse-reported bullying and documented nursing-sensitive patient outcomes. METHODS: Nurses (n = 432) in a large US hospital responded to a survey on workplace bullying. Unit-level data for 5 adverse patient events and nurse staffing were acquired from the National Database of Nursing Quality Indicators. Generalized linear models were used to examine the association between bullying and adverse patient events. A Bayesian regression analysis was used to confirm the findings. RESULTS: After controlling for nurse staffing and qualification, nurse-reported bullying was significantly associated with the incidence of central-line-associated bloodstream infections (P < .001). CONCLUSIONS: Interventions to address bullying, a malleable aspect of the nurse practice environment, may help to reduce adverse patient events.

Neutron Drip Line in the Ca Region from Bayesian Model Averaging.

The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent test. The recent discovery of the extremely neutron-rich nuclei around ^{60}Ca O. B. Tarasov et al. [Phys. Rev. Lett. 121, 022501 (2018)10.1103/PhysRevLett.121.022501] and the experimental determination of masses for ^{55-57}Ca S. Michimasa et al. [Phys. Rev. Lett. 121, 022506 (2018)10.1103/PhysRevLett.121.022506] provide unique information about the binding energy surface in this region. To assess the impact of these experimental discoveries on the nuclear landscape's extent, we use global mass models and statistical machine learning to make predictions, with quantified levels of certainty, for bound nuclides between Si and Ti. Using a Bayesian model averaging analysis based on Gaussian-process-based extrapolations we introduce the posterior probability p_{ex} for each nucleus to be bound to neutron emission. We find that extrapolations for drip-line locations, at which the nuclear binding ends, are consistent across the global mass models used, in spite of significant variations between their raw predictions. In particular, considering the current experimental information and current global mass models, we predict that ^{68}Ca has an average posterior probability p_{ex}≈76% to be bound to two-neutron emission while the nucleus ^{61}Ca is likely to decay by emitting a neutron (p_{ex}≈46%).