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Bayesian methods for hierarchical distance sampling models [PDF]
, 2014 Cornelia S. Oedekoven was supported by a studentship jointly funded by the University of St Andrews and EPSRC (EPSRC grant EP/C522702/1), through the National Centre for Statistical Ecology.The few distance sampling studies that use Bayesian methods ...
Oedekoven, Cornelia Sabrina +14 more
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Bayesian hierarchical model for bias-correcting climate models [PDF]
Geoscientific Model DevelopmentClimate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently ...
J. Carter +4 more
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Bayesian hierarchical models and prior elicitation for fitting psychometric functions
Frontiers in Computational Neuroscience, 2023 Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods.
Maura Mezzetti +7 more
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Joint segmentation of wind speed and direction using a hierarchical model [PDF]
, 2007 The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series.
Dobigeon, Nicolas, Tourneret, Jean-Yves
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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
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Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
Frontiers in Psychology, 2022 Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information ...
Endris Assen Ebrahim +2 more
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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
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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
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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 +5 more
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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 +5 more
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