A Bayesian analysis is used to detect a change-point in a sequence of independent random variables from exponential distributions. In This paper, we try to estimate change point which occurs in any sequence of independent exponential observations. The Bayes estimators are derived for change point, the rate of exponential distribution before shift and the rate of exponential distribution after s...

Neural Bayes estimators are neural networks that approximate estimators. They fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In this paper, we aim increase the awareness of statisticians relatively new inferential tool, facilitate its adoption by providing user-friendly open-source software. We also give attention ubiquitous problem estimating parameter...

We propose an adaptive shrinkage estimator for use in regression problems charaterized by many predictors, such as wavelet estimation. Adaptive estimators perform well over a variety of circumstances, such as regression models in which few, some or many coefficients are zero. Our estimator, PolyShrink, adaptively varies the amount of shrinkage to suit the estimation task. Whereas hard threshold...

The problem of inference about the binomial N parameter is considered. Applications arise in situations where an unknown population size is to be estimated. Previous work has focused on point estimation, but many applications require interval estimation, prediction, and decision-making. A Bayes empirical Bayes approach is presented. This provides a simple and flexible way of specifying prior in...