Results 31 to 40 of about 7,749,372 (301)

Grand Canonical Ensembles of Sparse Networks and Bayesian Inference

Entropy, 2022
Maximum entropy network ensembles have been very successful in modelling sparse network topologies and in solving challenging inference problems. However the sparse maximum entropy network models proposed so far have fixed number of nodes and are ...
Ginestra Bianconi
doaj   +1 more source

Hierarchic Bayesian models for kernel learning [PDF]

Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes ...
Mark A. Girolami, Simon Rogers
openaire   +1 more source

R/BHC : fast Bayesian hierarchical clustering for microarray data [PDF]

, 2009
Background: Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained ...
Grant, Murray, Katherine J Denby, Savage, R, Savage, Richard S., Xu, Yang, Denby Katherine J, Denby, Katherine J., David L Wild, Ghahramani, Z, Grant Murray, Richard S Savage, Truman, W, Truman, William M., Grant, M, Wild, David L., Murray Grant, Denby, K, Savage Richard S, William M Truman, Xu, Y, Ghahramani Zoubin, Truman William M, Heller Katherine, Katherine Heller, ,, Wild David L, Heller, KA, Heller, K. (Katherine), Yang Xu, Wild, DL, Ghahramani, Zoubin, Zoubin Ghahramani, Xu Yang   +32 more
core   +13 more sources

A Note on the Implementation of Hierarchical Dirichlet Processes [PDF]

, 2009
The implementation of collapsed Gibbs samplers for non-parametric Bayesian models is non-trivial, requiring con-siderable book-keeping. Goldwater et al.
Johnson, Mark, Phil Blunsom, Cohn, Trevor, Trevor Cohn, Sharon Goldwater, Mark Johnson, Blunsom, Phil, Goldwater, Sharon; id_orcid   +7 more
core   +1 more source

Bayesian Hierarchical Random Effects Models in Forensic Science

Frontiers in Genetics, 2018
Statistical modeling of the evaluation of evidence with the use of the likelihood ratio has a long history. It dates from the Dreyfus case at the end of the nineteenth century through the work at Bletchley Park in the Second World War to the present day.
Colin G. G. Aitken
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BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Journal of Statistical Software, 2021
Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model ...
Nikolas Kuschnig, Lukas Vashold
doaj   +1 more source

Using hierarchical Bayesian methods to examine the tools of decision-making [PDF]

Judgment and Decision Making, 2011
Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task.
Michael D. Lee, Benjamin J. Newell
doaj   +3 more sources

Hierarchical modeling of risk factors with and without prior information—the process of regression model evaluation for an example of respiratory diseases in piglet production from daily practice data

Frontiers in Veterinary Science
In veterinary epidemiology, regression models are commonly used to describe animal health and related risk factors. However, model selection and evaluation present ongoing challenges—especially when many potential predictors, complex interactions, and ...
Timur Tug, Fiona Mers, Franziska Schäkel, Doris Höltig, Lothar Kreienbrock, Katja Ickstadt   +5 more
doaj   +1 more source

Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets

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, Qiao Huijie, Zhang Yanzhou, Shi Weifeng, Ho Simon YW, Xu Weijun, Zhang Aibing, Zhu Chaodong   +7 more
doaj   +1 more source

Probabilistic inference: Task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modelling

Frontiers in Psychology, 2016
Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modelling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design ...
Moritz eBoos, Caroline eSeer, Florian eLange, Bruno eKopp   +3 more
doaj   +1 more source