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Bayesian hierarchical models

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
openaire   +2 more sources

Bayesian nonparametric hierarchical modeling

Biometrical Journal, 2009
AbstractIn 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 ...
openaire   +2 more sources

A Hierarchical Bayesian Choice Model with Visibility

2014 22nd International Conference on Pattern Recognition, 2014
We 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
openaire   +1 more source

Hierarchical Bayesian Models

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
openaire   +1 more source

Bayesian hierarchical modelling for process optimisation

International Journal of Production Research, 2020
Many industrial process optimisation methods rely on empirical models that relate output responses to a set of design variables.
Linhan Ouyang   +4 more
openaire   +1 more source

A Bayesian Hierarchical Model for Speech Enhancement

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
This 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
openaire   +1 more source

A Bayesian Hierarchical Model for the Evaluation of a Website

Journal of Applied Statistics, 2004
Consider 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
openaire   +3 more sources

Learning overhypotheses with hierarchical Bayesian models

Developmental Science, 2007
AbstractInductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired.
Kemp, C., Perfors, A., Tenenbaum, J.
openaire   +3 more sources

Bayesian Hierarchical Pointing Models

Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, 2022
Hang Zhao   +3 more
openaire   +1 more source

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