We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed have excellent comparative performance scale well to very large sample sizes due a binning strategy. Moreover, approach is fully...

Suppose X1,…,Xn is a random sample from bounded and decreasing density f0 on [0,?). We are interested in estimating such f0, with special interest f0(0). This problem encountered various statistical applications has gained quite some attention the literature. It well known that maximum likelihood estimator inconsistent at zero. led several authors to propose alternative estimators which consist...

The Bayesian estimation of the conditional Gaussian parameter needs to define several a priori parameters. The proposed approach is free from this definition of priors. We use the Implicit estimation method for learning from observations without a prior knowledge. We illustrate the interest of such an estimation method by giving first the Bayesian Expectation A Posteriori estimator for conditio...

This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...

In this paper we discuss ways to use Bayesian methodology to estimate the matching performance of a biometric identification device when no errors are detected. One of the drawbacks to the classical or frequentist statistical estimation methods is that it is not possible to create a confidence interval for the error rate when no errors are observed. In this paper we begin by discussing the rele...

Compressive Sensing (CS) has demonstrated to be particularly adapt for dealing with the directions-of-arrival (DoAs) estimation of electromagnetic signals impinging on an array of sensors. Unlike deterministic CS methods, the Bayesian CS (BCS) allows to overcome some theoretical limitations of the CS, such to enable a reliable and versatile DoAs estimation tool able to work with different array...