Results 31 to 40 of about 1,826,552 (219)
Bayesian Forecasting of Vector Moving Average Processes [PDF]
The Egyptian Statistical Journal, 2015 Forecasting is the final and one of the most important phases of a multivariate time series analysis. This article develops an approximate Bayesian methodology to forecast the future observations of vector moving average processes.
Sherif S.Ali
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Weyl Prior and Bayesian Statistics
Entropy, 2020 When using Bayesian inference, one needs to choose a prior distribution for parameters. The well-known Jeffreys prior is based on the Riemann metric tensor on a statistical manifold.
Ruichao Jiang +2 more
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Bayesian Estimation of a Linear Functional Relationship [PDF]
The Egyptian Statistical Journal, 1974 In this paper we give a Bayesian analysis to a linear functional relationship using Jeffreys' invariance rule to choose the prior distribution for the unknown parameters.
G. El-Sayyad
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Inference in Two-Piece Location-Scale Models with Jeffreys Priors [PDF]
Bayesian Analysis, 2014 This paper addresses the use of Jeffreys priors in the context of univariate threeparameter location-scale models, where skewness is introduced by differing scale parameters either side of the location. We focus on various commonly used parameterizations for these models.
Rubio, Francisco J., Steel, Mark F.J.
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Generalization of Jeffreys Divergence-Based Priors for Bayesian Hypothesis Testing [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2008 SummaryWe introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence-based (DB) priors. DB priors have simple forms and desirable properties like information (finite sample) consistency and are often similar to other existing proposals ...
Bayarri, M. J., García-Donato, G.
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THE BAYESIAN APPROACH AND THE PRECISION OF THE HERITABILITY ESTIMATE IN PERENNIAL SPECIES
Ciência Florestal, 2010 The objectives of the present work were to evaluate the precision of the estimate of heritability, which was determined by standard error, considering a Bayesian approach, and to compare such estimate with the classic procedure.
Freddy Mora +2 more
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Bayesian analysis for the Lomax model using noninformative priors
Statistical Theory and Related Fields, 2023 The Lomax distribution is an important member in the distribution family. In this paper, we systematically develop an objective Bayesian analysis of data from a Lomax distribution. Noninformative priors, including probability matching priors, the maximal
Daojiang He, Dongchu Sun, Qing Zhu
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Jeffrey's prior sampling of deep sigmoidal networks
CoRR, 2017 Neural networks have been shown to have a remarkable ability to uncover low dimensional structure in data: the space of possible reconstructed images form a reduced model manifold in image space. We explore this idea directly by analyzing the manifold learned by Deep Belief Networks and Stacked Denoising Autoencoders using Monte Carlo sampling.
Lorien X. Hayden +3 more
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Bayesian Prediction of Moving Average Processes Using Different Types of Priors [PDF]
The Egyptian Statistical Journal, 2018 The current article approaches the Bayesian prediction of moving average processes using three well-known priors: g prior, natural conjugate (NC) prior, and Jeffreys' prior.
Emad Soliman +2 more
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Emerging Themes in Epidemiology, 2021 Background One of the emerging themes in epidemiology is the use of interval estimates. Currently, three interval estimates for confidence (CI), prediction (PI), and tolerance (TI) are at a researcher's disposal and are accessible within the open access ...
Sonja Hartnack, Malgorzata Roos
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