Results 31 to 40 of about 157,395 (292)
BICOSS: Bayesian iterative conditional stochastic search for GWAS
BMC Bioinformatics, 2022 Background Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control.
Jacob Williams +2 more
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BMC Evolutionary Biology, 2010 Background Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data.
Luo Arong +7 more
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Bayesian Model Selection of Unified Neutron Star EOSs in Multi-messenger Era
Yuanzineng kexue jishu, 2023 The equation of state (EoS) of neutron star matter plays a key role in both the structure and evolution of a neutron star. However, as lattice QCD faces significant difficulties in simulating dense matter, only effective models can be relied on to unveil
RUI Xingyu;MIAO Zhiqiang;XIA Chengjun
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Bayesian model selection for LISA pathfinder [PDF]
Physical Review D, 2014 The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF ...
Nikolaos Karnesis +14 more
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Data-driven prediction and origin identification of epidemics in population networks [PDF]
Royal Society Open Science, 2021 Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for
Karen Larson +7 more
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Bayesian model selection of stochastic block models [PDF]
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016 A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for ...
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PAC-Bayesian Stochastic Model Selection [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On model selection in cosmology
SciPost Physics Lecture Notes, 2019 We review some of the common methods for model selection: the goodness of fit, the likelihood ratio test, Bayesian model selection using Bayes factors, and the classical as well as the Bayesian information theoretic approaches.
Martin Kerscher, Jochen Weller
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Test optimization selection method based on NSGA-3 and improved Bayesian network model
Xibei Gongye Daxue Xuebao, 2021 Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations.
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Bayesian selection of graphical regulatory models
International Journal of Approximate Reasoning, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Silvia Liverani, Jim Q. Smith
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