Additive Bayesian Network Modeling with the R Package abn

The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production

Additive Bayesian Network Modeling with the R Package abn

The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network. It supports a possible blend of continuous, discrete and count data and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionalities using a veterinary dataset about respiratory diseases in commercial swine production

Additive Bayesian Network Modelling with the R Package abn

The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network. It supports a possible blend of continuous, discrete and count data and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionalities using a veterinary dataset about respiratory diseases in commercial swine production.Comment: 37 pages, 14 figures and 2 table

Predicting LyC emission of galaxies using their physical and Lyα\alpha emission properties

The primary difficulty in understanding the sources and processes that powered cosmic reionization is that it is not possible to directly probe the ionizing Lyman Continuum (LyC) radiation at that epoch as those photons have been absorbed by the intervening neutral hydrogen in the IGM on their way to us. It is therefore imperative to build a model to accurately predict LyC emission using other properties of galaxies in the reionization era. In recent years, studies have shown that the LyC emission from galaxies may be correlated to their Lya emission. Here, we study this correlation by analyzing thousands of galaxies at high-z in the SPHINX cosmological simulation. We post-process these galaxies with the Lya radiative transfer code RASCAS and analyze the Lya - LyC connection. We find that the Lya and LyC luminosities are strongly correlated with each other, although with dispersion. There is a positive correlation between Lya and LyC escape fractions in the brightest Lya emitters (>$10^{41}$ erg/s), similar to the recent observational studies. However, when we also include fainter Lya emitters (LAEs), the correlation disappears, which suggests that the observed relationship may be driven by selection effects. We also find that bright LAEs are dominant contributors to reionization ($> 10^{40}$ erg/s galaxies contribute $> 90\%$ of LyC emission). Finally, we build predictive models using multivariate linear regression where we use the physical and the Lya properties of simulated galaxies to predict their intrinsic and escaping LyC luminosities with a high degree of accuracy. We find that the most important galaxy properties to predict the escaping LyC luminosity of a galaxy are its escaping Lya luminosity, gas mass, gas metallicity, and SFR. These models can be very useful to predict LyC emissions from galaxies and can help us identify the sources of reionization.Comment: Accepted to Astronomy and Astrophysics (A&A) Journal. 27 pages, 21 Figure

Association of maternal lipid levels with birth weight and cord blood insulin: a Bayesian network analysis

Objective: To assess the independent association of maternal lipid levels with birth weight and cord blood insulin (CBI) level. Setting: The Born in Guangzhou Cohort Study, Guangzhou, China. Participants: Women who delivered between January 2015 and June 2016 and with umbilical cord blood retained were eligible for this study. Those with prepregnancy health conditions, without an available fasting blood sample in the second trimester, or without demographic and glycaemic information were excluded. After random selection, data from 1522 mother–child pairs were used in this study. Exposures and outcome measures: Additive Bayesian network analysis was used to investigate the interdependency of lipid profiles with other metabolic risk factors (prepregnancy body mass index (BMI), fasting glucose and early gestational weight gain) in association with birth weight and CBI, along with multivariable linear regression models. Results: In multivariable linear regressions, maternal triglyceride was associated with increased birth weight (adjusted β=67.46, 95% CI 41.85 to 93.06 g per mmol/L) and CBI (adjusted β=0.89, 95% CI 0.06 to 1.72 μU/mL per mmol/L increase), while high-density lipoprotein cholesterol was associated with decreased birth weight (adjusted β=−45.29, 95% CI −85.49 to −5.09 g per mmol/L). After considering the interdependency of maternal metabolic risk factors in the Network analysis, none of the maternal lipid profiles was independently associated with birth weight and CBI. Instead, prepregnancy BMI was the global strongest factor for birth weight and CBI directly and indirectly. Conclusions: Gestational dyslipidaemia appears to be secondary to metabolic dysfunction with no clear association with metabolic adverse outcomes in neonates. Maternal prepregnancy overweight/obesity appears the most influential upstream metabolic risk factor for both maternal and neonatal metabolic health; these data imply weight management may need to be addressed from the preconception period and during early pregnancy