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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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Bayesian hierarchical model for protein identifications [PDF]
Journal of Applied Statistics, 2018 In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, mass spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a ‘two-step’ procedure of identifying the peptide first followed by the ...
Riten, Mitra +3 more
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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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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
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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
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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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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
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Bayesian designs for hierarchical linear models [PDF]
Statistica Sinica, 2009 Summary: Two Bayesian optimal design criteria for hierarchical linear models are discussed: the \(\psi_\beta\) criterion for the estimation of individual-level parameters \(\beta\), and the \(\psi_\theta\) criterion for the estimation of hyperparameters \(\mathbf \theta\).
Liu, Qing +2 more
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Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach [PDF]
Advances in Geosciences, 2010 The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors.
I. Hussain, J. Pilz, G. Spoeck
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