Bayesian computation for the common coefficient of variation of delta-lognormal distributions with application to common rainfall dispersion in Thailand [PDF]
PeerJ, 2022 Rainfall fluctuation makes precipitation and flood prediction difficult. The coefficient of variation can be used to measure rainfall dispersion to produce information for predicting future rainfall, thereby mitigating future disasters.
Noppadon Yosboonruang +2 more
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Measuring the dispersion of rainfall using Bayesian confidence intervals for coefficient of variation of delta-lognormal distribution: a study from Thailand [PDF]
PeerJ, 2019 Since rainfall data series often contain zero values and thus follow a delta-lognormal distribution, the coefficient of variation is often used to illustrate the dispersion of rainfall in a number of areas and so is an important tool in statistical ...
Noppadon Yosboonruang +2 more
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مجلة بغداد للعلوم, 2013 In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two ...
Baghdad Science Journal
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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 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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