which can't be prevented by vaccines or cured by medication, nor do they disappear. It persists for a long time and tends to be more common with age. To obtain the previous information of the diseases, the authors use non-parametric Bayesian hazard regression and posterior predictive inference...
Image segmentationIn this paper, a Bayesian framework for non-parametric density estimation with spatial smoothness constraints is presented for image segmentation. Unlike common parametric methods such as mixtures of Gaussians, the proposed method does not make strict assumptions about the shape of the...
1) non-parametric bayesian model 非参数化Bayes模型2) non-parametric model 非参数化模型 1. At the same time,based on the experimental results,the paper applies back-propagation network to build the non-parametric model,and uses the model to forecast the characteristics of the output force. ...
We developed a flexible non-parametric Bayesian model for regional disease-prevalence estimation based on cross-sectional data that are obtained from several subpopulations or clusters such as villages, cities, or herds. The subpopulation prevalences are modeled with a mixture distribution that allows ...
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Integro-difference equations (IDEs) provide a flexible framework for dynamic modeling of spatio-temporal data. The choice of kernel in an IDE model relates directly to the underlying physical process modeled, and it can affect model fit and predictive accuracy. We introduce Bayesian non-parametric ...
Dunson. Marginally specified priors for non-parametric Bayesian estimation. J R Stat Soc Series B Stat Methodol, 77(1): 35-58, 2015.Kessler, D., Hoff, P., and Dunson, D. (2012). "Marginally Specified Priors for Non- parametric Bayesian Estimation." Technical Report 596, Department of ...
Bayesian analysis of survival on multiple time scales We propose a Bayesian approach to the analysis of survival data on multiple time scales. Non-parametric modelling of variation of rates with more than one ... C Berzuini,D Clayton - 《Statistics in Medicine》 被引量: 179发表: 2010年 REM...
Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, ...
Multi-modal distance metric learning: A bayesian non-parametric approach. In European Conference on Computer Vision, pages 63-77. Springer, 2014.Behnam Babagholami-Mohamadabadi, Seyed Mahdi Roostaiyan, Ali Zarghami, and Mahdieh Soleymani Baghshah, "Multi-modal distance metric learning: Abayesian ...