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Comments on: "Hybrid Semiparametric Bayesian Networks" [PDF]
TEST (2022), 30, 328-330, 2022 Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks" by David Atienza, Pedro Larranaga and Concha Bielza (TEST, 2022).
arxiv
Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
, 2016 Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies.
Banks, David +5 more
core +1 more source
A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors
Frontiers in Neurology, 2018 Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction. To satisfy the core requirement in medical research to obtain interpretable prediction
Eunjeong Park, H. Chang, H. Nam
semanticscholar +1 more source
Spectral Bayesian network theory
Linear Algebra and its Applications, 2023 22 pages, 6 ...
Luke Duttweiler +2 more
openaire +3 more sources
Digital Health, 2022 Background Community-acquired pneumonia is one of the most common infectious diseases in children and is a leading cause of death among children under 5 years of age, resulting in high rates of antibiotic usage and hospitalization.
Jing Li +10 more
doaj +1 more source
Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin
, 2017 Current airframe health monitoring generally relies on deterministic physics models and ground inspections.
Chenzhao Li +4 more
semanticscholar +1 more source
Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling [PDF]
Integrated Environmental Assessment and Management, 2020 Abstract The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties.
Raoul Wolf +7 more
openaire +7 more sources
Identification of novel small molecule inhibitors of ETS transcription factors
FEBS Letters, EarlyView.ETS transcription factors play an essential role in tumourigenesis and are indispensable for sprouting angiogenesis, a hallmark of cancer, which fuels tumour expansion and dissemination. Thus, targeting ETS transcription factor function could represent an effective, multifaceted strategy to block tumour growth. The evolutionarily conserved E‐Twenty‐Six
Shaima Abdalla +9 more
wiley +1 more source
Network Formation with Asymmetric Players and Chance Moves
Mathematics, 2021 We propose a model of network formation as a two-stage game with chance moves and players of various types. First, the leader suggests a connected communication network for the players to join.
Ping Sun, Elena Parilina
doaj +1 more source
Markov Equivalence of Max-Linear Bayesian Networks [PDF]
arXiv, 2021 Max-linear Bayesian networks have emerged as highly applicable models for causal inference via extreme value data. However, conditional independence (CI) for max-linear Bayesian networks behaves differently than for classical Gaussian Bayesian networks. We establish the parallel between the two theories via tropicalization, and establish the surprising
arxiv