Results 21 to 30 of about 157,395 (292)
NetDiff - Bayesian model selection for differential gene regulatory network inference. [PDF]
Sci Rep, 2016 Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response ...
Thorne T.
europepmc +5 more sources
Radiometric Scale Transfer Using Bayesian Model Selection
Proceedings, 2020 The key input quantity to climate modelling and weather forecasts is the solar beam irradiance, i.e., the primary amount of energy provided by the sun.
Donald W. Nelson, Udo von Toussaint
doaj +1 more source
Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]
PLoS ONE, 2014 Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
doaj +1 more source
Water Resources Research, 2023 Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models through the use of Bayesian model evidence (BME).
Maria Fernanda Morales Oreamuno +2 more
doaj +1 more source
Bayesian Reordering Model with Feature Selection [PDF]
Proceedings of the Ninth Workshop on Statistical Machine Translation, 2014 In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language.
Alrajeh, Abdullah, Niranjan, Mahesan
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Data and code for Bayesian model selection manuscript
, 2021 Data and code used for the Bayesian model selection manuscript. Data includes liver tissue protein abundance and genotypes from ~200 Diversity Outbred mice and gene expression and chromatin accessibility from human cell lines.
Greg Keele (7501151) +1 more
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Robust and parallel Bayesian model selection [PDF]
Computational Statistics & Data Analysis, 2018 Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that damage the inference quality.
Michael Minyi Zhang +2 more
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Bayesian model averaging: improved variable selection for matched case-control studies
Epidemiology, Biostatistics and Public Health, 2019 Background: The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field.
Yi Mu +2 more
doaj +1 more source
Bayesian model selection for group studies [PDF]
NeuroImage, 2009 Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM).
Klaas Enno Stephan +4 more
openaire +6 more sources
An introduction to Bayesian model selection for evaluating informative hypotheses. [PDF]
, 2011 Most researchers have specific expectations concerning their research questions. These may be derived from theory, empirical evidence, or both. Yet despite these expectations, most investigators still use null hypothesis testing to evaluate their data ...
Meeus, W.H.J. +13 more
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