Results 41 to 50 of about 539,449 (277)

Correction: Minimum uncertainty as Bayesian network model selection principle [PDF]

BMC Bioinformatics
Grigoriy Gogoshin, Andrei S. Rodin
doaj   +2 more sources

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 ...
openaire   +2 more sources

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, Georgios Arampatzis, Clark Bowman, Zhizhong Chen, Panagiotis Hadjidoukas, Costas Papadimitriou, Petros Koumoutsakos, Anastasios Matzavinos   +7 more
doaj   +1 more source

Bayesian Exponential Random Graph Models with Nodal Random Effects [PDF]

, 2015
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network.
A. Caimo, Caimo, Caimo, Caimo, Chatterjee, Everitt, Fienberg, Frank, G. Kauermann, Gelman, Geyer, Gill, Goldenberg, Holland, Hunter, Hunter, Hunter, Hunter, Kapferer, Kass, Kolaczyk, Krivitsky, Lusher, Milgram, Murray, N. Friel, R Core Team, Robins, Robins, Robins, Robins, S. Thiemichen, Salter-Townshend, Schweinberger, Severini, Snijders, Spiegelhalter, Strauss, Thurner, van Duijn, Varin, Watts, Zachary, Zijlstra   +43 more
core   +3 more sources

Bayesian graph selection consistency under model misspecification

Bernoulli, 2021
Gaussian graphical models are a popular tool to learn the dependence structure in the form of a graph among variables of interest. Bayesian methods have gained in popularity in the last two decades due to their ability to simultaneously learn the covariance and the graph and characterize uncertainty in the selection. For scalability of the Markov chain
Niu, Yabo, Pati, Debdeep, Mallick, Bani K.   +2 more
openaire   +5 more sources

Universal Darwinism as a process of Bayesian inference

Frontiers in Systems Neuroscience, 2016
Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these ...
John Oberon Campbell
doaj   +1 more source

Entropic Priors and Bayesian Model Selection

, 2009
We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured, weakening the usual ...
Brendon J. Brewer, Chun-Yong Chan, Matthew J. Francis, Paul M. Goggans   +3 more
core   +1 more source

Consistency of Bayesian Linear Model Selection With a Growing Number of Parameters [PDF]

, 2011
Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which provide a stochastic search of informative variables,
Clayton, Murray K., Shang, Zuofeng
core   +2 more sources

Quasi-Bayesian model selection

Quantitative Economics, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Inoue, Atsushi, Shintani, Mototsugu
openaire   +3 more sources

Training samples in objective Bayesian model selection

, 2004
Central to several objective approaches to Bayesian model selection is the use of training samples (subsets of the data), so as to allow utilization of improper objective priors.
Berger, James O., Pericchi, Luis R.
core   +3 more sources