Results 71 to 80 of about 3,103,450 (291)

Relational Bayesian Networks

CoRR, 1997
A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network.
openaire   +4 more sources

Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks [PDF]

, 2009
Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be ...
Husmeier, D., Marco Grzegorczyk, Dirk Husmeier, Grzegorczyk, M.   +3 more
core   +1 more source

CausalTrail: Testing hypothesis using causal Bayesian networks [version 1; referees: 2 approved]

F1000Research, 2015
Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system,
Daniel Stöckel, Florian Schmidt, Patrick Trampert, Hans-Peter Lenhof   +3 more
doaj   +1 more source

Analyzing Uncertainty in Complex Socio-Ecological Networks

Entropy, 2020
Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain.
Ana D. Maldonado, María Morales, Pedro A. Aguilera, Antonio Salmerón   +3 more
doaj   +1 more source

Achieving career satisfaction through fostering innovation: lessons from the engineering profession in the Australian public sector

Engineering Management in Production and Services, 2021
This paper proposes a novel approach that integrates the capability of empirical validation of structural equation modelling (SEM) and the prediction ability of Bayesian networks (BN).
Wipulanusat Warit, Panuwatwanich Kriengsak, Stewart Rodney A., Sunkpho Jirapon, Thamsatitdej Poomporn   +4 more
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

The variance of causal effect estimators for binary v-structures

Journal of Causal Inference, 2022
Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
doaj   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Efficient utility-based clustering over high dimensional partition spaces [PDF]

, 2009
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ, Millar, A. J., Millar, AJ, Liverani, Silvia, Smith, J. Q., Liverani, S, Anderson, Paul E., Anderson, PE, Edwards, Kieron D., Edwards, KD   +9 more
core   +1 more source