Results 271 to 280 of about 758,750 (320)

Hierarchical Bayesian models of cognitive development

Biological Cybernetics, 2016
This article provides an introductory overview of the state of research on Hierarchical Bayesian Modeling in cognitive development. First, a brief historical summary and a definition of hierarchies in Bayesian modeling are given. Subsequently, some model structures are described based on four examples in the literature.
Thomas, Glassen, Verena, Nitsch
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Bayesian Hierarchical Modelling (BHM)

2020
In the previous chapters, our statistical procedure was very simple: define a prior probability distribution for the parameters \(p[\theta ]\) and a likelihood function \(L[\theta ]=p[y|\theta ]\), and that was it. Bayes’ theorem then told us what the posterior distribution would be once we received the data: \(p[\theta |y] \propto p[\theta ] L[\theta ]
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Hierarchical Bayesian continuous time dynamic modeling.

Psychological methods, 2018
Continuous time dynamic models are similar to popular discrete time models such as autoregressive cross-lagged models, but through use of stochastic differential equations can accurately account for differences in time intervals between measurements, and
Charles C. Driver, M. Voelkle
semanticscholar   +1 more source

Hierarchical Bayesian space-time models

Environmental and Ecological Statistics, 1998
Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations.
Wikle, Christopher, Berliner, L M, Cressie, Noel A   +2 more
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Bayesian Hierarchical Models for Subgroup Analysis

Pharmaceutical Statistics
ABSTRACTIn conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the ...
Yun Wang, Wenda Tu, William Koh, James Travis, Robert Abugov, Kiya Hamilton, Mengjie Zheng, Roberto Crackel, Pablo Bonangelino, Mark Rothmann   +9 more
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Bayesian Hierarchical Response Modeling

2010
In the _rst chapter, an introduction to Bayesian item response modeling was given. The Bayesian methodology requires careful speci_cation of priors since item response models contain many parameters, often of the same type. A hierarchical modeling approach is introduced that supports the pooling of information to improve the precision of the parameter ...
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Bayesian Hierarchical Pointing Models

Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, 2022
Hang Zhao, Sophia Gu, Chun Yu, Xiaojun Bi   +3 more
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Representing credal imprecision: from sets of measures to hierarchical Bayesian models

Philosophical Studies, 2020
Daniel Lassiter
semanticscholar   +1 more source

Bayesian Hierarchical Models

JAMA, 2018
Anna E, McGlothlin, Kert, Viele
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Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Xiaogang Wang, Xiaoxu Ma, W. Grimson
semanticscholar   +1 more source