Results 11 to 20 of about 50,503 (261)
Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]
Royal Society Open Science, 2019 Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel +2 more
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A hierarchical Bayesian model for frame representation [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients.
Chaari, Lotfi +4 more
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A Hierarchical Bayesian Model for Crowd Emotions [PDF]
Frontiers in Computational Neuroscience, 2016 Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions.
Oscar J. Urizar +5 more
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Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data. [PDF]
PLoS ONE, 2015 It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a ...
Ting Tian +3 more
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Sensors, 2020 Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data.
Mingming Song +3 more
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Hierarchical Bayesian Models for Multiple Count Data
Austrian Journal of Statistics, 2016 The aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it
Radu Tunaru
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Bayesian hierarchical models and prior elicitation for fitting psychometric functions
Frontiers in Computational Neuroscience, 2023 Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods.
Maura Mezzetti +7 more
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Software for Bayesian Statistics
Journal of Statistical Software, 2021 In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model
Michela Cameletti, Virgilio Gómez-Rubio
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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
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Bayesian hierarchical model for bias-correcting climate models [PDF]
Geoscientific Model DevelopmentClimate 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 +4 more
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