Results 221 to 230 of about 50,616 (261)
Sensitivity Analysis for Bayesian Hierarchical Models
Bayesian Analysis, 2015 Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the parameters within deeper layers in the hierarchy becomes challenging. In addition, lack of information together with identifiability issues may imply that the prior distributions for
Malgorzata Roos +2 more
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2000
Publisher Summary This chapter describes the Bayesian hierarchical models. The evaluation of this study, by a Bayesian hierarchical linear model is derived from the data that include the other large clinical trials of thrombolytic therapy and suggests that treatment is also beneficial for patients, arriving much later than six hours after symptom ...
C H, Schmid, E N, Brown
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Publisher Summary This chapter describes the Bayesian hierarchical models. The evaluation of this study, by a Bayesian hierarchical linear model is derived from the data that include the other large clinical trials of thrombolytic therapy and suggests that treatment is also beneficial for patients, arriving much later than six hours after symptom ...
C H, Schmid, E N, Brown
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Bayesian nonparametric hierarchical modeling
Biometrical Journal, 2009AbstractIn biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects ...
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A Hierarchical Bayesian Choice Model with Visibility
2014 22nd International Conference on Pattern Recognition, 2014We extend the standard choice model of multinomial logit model (MLM) into a hierarchical Bayesian model to simultaneously estimate the preferences of customers and the visibility of items from purchasing history. We say that an item has high visibility when customers well consider that item as a candidate before making a choice.
Takayuki Osogami, Takayuki Katsuki
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2015
This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology ...
N. Thompson Hobbs, Mevin B. Hooten
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This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology ...
N. Thompson Hobbs, Mevin B. Hooten
exaly +2 more sources
A Bayesian Hierarchical Model for Speech Enhancement
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018This paper addresses the problem of blind adaptive beamforming using a hierarchical Bayesian model. Our probabilistic approach relies on a Gaussian prior for the speech signal and a Gamma hyperprior for the speech precision, combined with a multichannel linear-Gaussian state-space model for the possibly time-varying acoustic channel.
Yaron Laufer, Sharon Gannot
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A Bayesian Hierarchical Model for the Evaluation of a Website
Journal of Applied Statistics, 2004Consider a website and the surfers visiting its pages. A typical issue of interest, for example while monitoring an advertising campaign, concerns whether a specific page has been designed successfully, i.e. is able to attract surfers or address them to other pages within the site.
L. DI SCALA, LA ROCCA, Luca, G. CONSONNI
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Bayesian hierarchical modelling for process optimisation
International Journal of Production Research, 2020Many industrial process optimisation methods rely on empirical models that relate output responses to a set of design variables.
Linhan Ouyang +4 more
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