Results 31 to 40 of about 7,662,414 (297)

Software for Bayesian Statistics

Journal of Statistical Software, 2021
In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model
Michela Cameletti, Virgilio Gómez-Rubio
doaj   +1 more source

Modelling unexpected failures with a hierarchical Bayesian model [PDF]

2017 2nd International Conference on System Reliability and Safety (ICSRS), 2017
Systems, especially those in the design and development phase, frequently suffer from unexpected failures, which are caused by insufficient knowledge of the system failure processes. In this paper, we develop a hierarchical Bayesian reliability model that account for unexpected failures.
Zeng, Zhiguo, Zio, Enrico
openaire   +4 more sources

A Bayesian hierarchical model for allele frequencies [PDF]

Genetic Epidemiology, 2000
Genetic epidemiological methodologies, such as linkage analysis, often require accurate estimates of allele frequencies. When studies involve multiple sub-populations with different evolutionary histories, accurate estimates can be difficult to obtain because the number of subjects per sub-population tends to be limited.
J R, Lockwood, K, Roeder, B, Devlin
openaire   +2 more sources

Latent diffusion models for survival analysis [PDF]

, 2010
We consider Bayesian hierarchical models for survival analysis, where the survival times are modeled through an underlying diffusion process which determines the hazard rate.
Laura M. Sangalli, G. O. Roberts, SANGALLI, LAURA MARIA, Roberts, Gareth O., Sangalli, Laura M., Gareth O. Roberts   +5 more
core   +1 more source

conting : an R package for Bayesian analysis of complete and incomplete contingency tables [PDF]

, 2014
The aim of this paper is to demonstrate the R package conting for the Bayesian analysis of complete and incomplete contingency tables using hierarchical log-linear models.
Ruth King, Overstall, Antony, Antony M. Overstall, King, Ruth, Antony M. Overstall, Overstall, Antony M.   +5 more
core   +1 more source

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

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