Results 121 to 130 of about 6,917,983 (390)
On the relevance of prognostic information for clinical trials: A theoretical quantification
Biometrical Journal, Volume 65, Issue 1, January 2023., 2023 Abstract The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalization.
Sandra Siegfried +2 more
wiley +1 more source
The Value of Device Characterization for the Optimization of Organic Solar Cells
Advanced Energy Materials, EarlyView.Using the example of organic photovoltaics (OPV), this study examines whether and when additional measurements can be helpful in process optimization. A virtual laboratory based on real solar cells serves as a benchmark function to compare two different approaches for process optimization, namely black‐box optimization (black circle) and model‐based ...
Leonard Christen +4 more
wiley +1 more source
arXiv, 2001 Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations.
arxiv
An Empirical-Bayes Score for Discrete Bayesian Networks
, 2016 Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidate structures using their posterior probabilities for a given data set.
Scutari, Marco
core
Bayesian Approach to Network Modularity
Physical Review Letters, 2008 We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem ...
Jake M. Hofman, Chris H. Wiggins
openaire +5 more sources
Applied Economic Perspectives and Policy, EarlyView.Abstract Extreme weather events are worsening the fragile rural infrastructure in the United States, impacting trade flows of agricultural products. The Mississippi River, vital for transporting agricultural commodities, reached historic lows during the 2022 and 2023 fall harvests, increasing transportation costs and lowering crop prices.
James L. Mitchell, Hunter D. Biram
wiley +1 more source
Discrete Bayesian Network Classifiers
ACM Computing Surveys, 2014 We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge.
C. Bielza, P. Larrañaga
semanticscholar +1 more source
Agribusiness, EarlyView.Abstract This study examines producer participation choices considering a variety of potential benefits linked to state‐sponsored marketing programs, using a real choice dataset of farmers in Missouri. Multinomial logit models are employed to predict determinants of farmer enrollment in three tiers of the Missouri Grown local food marketing program ...
Lan Tran, Ye Su, Laura McCann
wiley +1 more source
Agribusiness, EarlyView.ABSTRACT This study investigates the financial literacy (FL) of Swedish farmers, its linkages to farmer characteristics, management accounting practices and farm outcomes by surveying Swedish Farm Accountancy Data Network farmers. Using item response theory, we expand the existing FL measurement specifically to the farming context, assess measurement ...
Uliana Gottlieb, Helena Hansson
wiley +1 more source
On the Relative Expressiveness of Bayesian and Neural Networks [PDF]
arXiv, 2018 A neural network computes a function. A central property of neural networks is that they are "universal approximators:" for a given continuous function, there exists a neural network that can approximate it arbitrarily well, given enough neurons (and some additional assumptions).
arxiv