Non-Bayesian Parametric Missing-Mass Estimation

Marginally specified priors for non-parametric Bayesian estimation.

Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new fr...

the estimation of survival function for colon cancer data in tehran using non-parametric bayesian model

background: colon cancer is the third cause of cancer deaths. although colon cancer survival time has increased in recent years, the mortality rate is still high. the cox model is the most common regression model often used in medical research in survival analysis, but most of the time the effect of at least one of the independent factors changes over time, so the model cannot be used. in the c...

Non–parametric volatility estimation

Non-parametric Bayesian super-resolution

Super-resolution of signals and images can improve the automatic detection and recognition of objects of interest. However, the uncertainty associated with this process is not often taken into consideration. This is important because the processing of noisy signals can result in spurious estimates of the scene content. This paper reviews a variety of super-resolution techniques and presents two...

Bayesian non-parametric parsimonious clustering

This paper proposes a new Bayesian non-parametric approach for clustering. It relies on an infinite Gaussian mixture model with a Chinese Restaurant Process (CRP) prior, and an eigenvalue decomposition of the covariance matrix of each cluster. The CRP prior allows to control the model complexity in a principled way and to automatically learn the number of clusters. The covariance matrix decompo...