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Sufficiency in bayesian models [PDF]
Applicationes Mathematicae, 1998 Using the Bayesian setting the authors show that sufficiency, prediction sufficiency and freedom are special cases of conditional independence. Using this approach new and known results between sufficiency, prediction sufficiency and freedom, and especially Basu's theorem, are established.
Furmańczyk, K., Niemiro, W.
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Plants, 2020 High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this
Paulina Ballesta +3 more
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Bayesian Models of Mentalizing [PDF]
Brain Topography, 2008 Surprisingly effortless is the human capacity known as "mentalizing", i.e., the ability to explain and predict the behavior of others by attributing to them independent mental states, such as beliefs, desires, emotions or intentions. This capacity is, among other factors, dependent on the correct anticipation of the dynamics of facially expressed ...
Grave de Peralta Menendez, Rolando +4 more
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Bayesian methods for analyzing true-and-error models [PDF]
Judgment and Decision Making, 2018 Birnbaum and Quispe-Torreblanca (2018) evaluated a set of six models developed under true-and-error theory against data in which people made choices in repeated gambles.
Michael D. Lee
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Benchmarking and Reconciliation With Time-Varying Cross-Coefficients
Methodology, 2020 In this paper, the authors propose a method to obtain explicit solutions for simultaneous benchmarking and reconciliation problems for a system of variables when the cross-restrictions use time-varying coefficients.
José Luis Rojo-García +1 more
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Bayesian selection of plane-wave decomposition models [PDF]
JASA Express Letters, 2023 Plane-wave decompositions, whereby a measured sound field is described as a superposition of plane waves, are central to many applications in acoustics and audio engineering.
Mélanie Nolan, Ning Xiang
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Bayesian calibration of firn densification models [PDF]
The Cryosphere, 2020 Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying
V. Verjans +6 more
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Bayesian Multilevel Models for Count Data
Journal of Nigerian Society of Physical Sciences, 2021 The traditional Poisson regression model for fitting count data is considered inadequate to fit over-or under-dispersed count data and new models have been developed to make up for such inadequacies inherent in the model.
Olumide Sunday Adesina
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Bayesian Approach for Analyzing Computer Models using Gaussian Process Models. [PDF]
مجلة التربية والعلم, 2021 Mathematical models, usually implemented in computer programs known as computer models, are widely used in all areas of science and technology to represent complex systems in the real world. However, computer models are often so complex in such that they
hasan Saeid, Younus Al-Taweel
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Computational Statistics & Data Analysis, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Denison, D, Adams, N, Holmes, C, Hand, D
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