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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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A subsampling approach for Bayesian model selection
International Journal of Approximate Reasoning, 2022 33 pages, 17 figures ...
Jon Lachmann +3 more
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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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PAC-Bayesian Stochastic Model Selection [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bayesian model averaging: improved variable selection for matched case-control studies
Epidemiology, Biostatistics and Public Health, 2022 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, Isaac See, Jonathan R. Edwards
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Bayesian Model Averaging, Learning, and Model Selection* [PDF]
, 2013 Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models.
Evans, George W. +3 more
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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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Bayesian Online Model Selection
CoRROnline model selection in Bayesian bandits raises a fundamental exploration challenge: When an environment instance is sampled from a prior distribution, how can we design an adaptive strategy that explores multiple bandit learners and competes with the best one in hindsight?
Aida Afshar, Yuke Zhang, Aldo Pacchiano
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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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Statistical Theory and Related Fields, 2020 The power-expected-posterior prior is used in this paper for comparing nested linear models. The asymptotic behaviour of the method is investigated for different values of the power parameter of the prior.
D. Fouskakis +2 more
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