Results 21 to 30 of about 758,750 (320)

Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior

Stats, 2022
We discuss a Bayesian hierarchical copula model for clusters of financial time series. A similar approach has been developed in recent paper. However, the prior distributions proposed there do not always provide a proper posterior. In order to circumvent
Paolo Onorati, Brunero Liseo
doaj   +1 more source

CARBayes: an R package for Bayesian spatial modeling with conditional autoregressive priors [PDF]

, 2013
Conditional autoregressive models are commonly used to represent spatial autocorrelation in data relating to a set of non-overlapping areal units, which arise in a wide variety of applications including agriculture, education, epidemiology and image ...
Lee, Duncan
core   +3 more sources

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors [PDF]

, 2006
We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure.
A Clare, A McCallum, AS Weigend, B Rost, B Schoikowski, B Shahbaba, Babak Shahbaba, BE Engelhardt, D Koller, EM Marcotte, FR Blattner, H Blockeel, I Tsochantaridis, IUBMB, J DeRisi, J Fox, J Goodman, J Struyf, J Zhang, JA Eisen, JR Guest, K Sjölander, L Cai, L Dehaspe, M Brown, M Deng, M Deng, M Eisen, M Riley, M Riley, N Cesa-Bianchi, O Dekel, P Pavlidis, R Caruana, R Eisner, Radford M Neal, RD King, RD King, RM Neal, RM Neal, RM Neal, S Rison, S Sattath, S Spiro, SF Altschul, ST Dumais, WR Pearson, Z Barutcuoglu   +47 more
core   +4 more sources

Bayesian hierarchical model for bias-correcting climate models [PDF]

Geoscientific Model Development
Climate 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, J. Carter, E. A. Chacón-Montalván, A. Leeson, A. Leeson   +4 more
doaj   +1 more source

Bayesian hierarchical models for linear networks [PDF]

Journal of Applied Statistics, 2020
The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process.
Zainab, Al-Kaabawi, Yinghui, Wei, Rana, Moyeed   +2 more
openaire   +2 more sources

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, Dean, Angela, Allenby, Gregory   +2 more
openaire   +3 more sources

3D extinction mapping using hierarchical Bayesian models [PDF]

, 2012
The Galaxy and the stars in it form a hierarchical system, such that the properties of individual stars are influenced by those of the Galaxy. Here, an approach is described which uses hierarchical Bayesian models to simultaneously and empirically ...
Stuart E. Sale, Stuart E. Sale
semanticscholar   +1 more source

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

Bayesian hierarchical modeling of size spectra

Methods in Ecology and Evolution, 2023
AbstractA fundamental pattern in ecology is that smaller organisms are more abundant than larger organisms. This pattern is known as the individual size distribution (ISD), which is the frequency of all individual body sizes in an ecosystem.The ISD is described by a power law and a major goal of size spectra analyses is to estimate the exponent of the ...
Jeff S. Wesner, Justin P. F. Pomeranz, James R. Junker, Vojsava Gjoni   +3 more
openaire   +3 more sources