We propose a class of Bayesian nonparametric mixture models with a Beta distribution providing the mixture kernel and a Dirichlet process prior assigned to the mixing distribution. Motivating applications include density estimation on bounded domains, and inference for non-homogeneous Poisson processes over time. We present the mixture model formulation, discuss prior specification, and develop...

Abst rac t . Most existing studies on software release policies use models based on the non-homogeneous Poisson process. In this paper, we discuss a software release policy based on a state space model. The state space model has a Gamma-Gamma type invariant conditional distribution. A cost model that removes errors in software systems and risk cost due to software failure is used. The optimal r...

In this article we consider the problem of estimating the intensity of a nonhomogeneous point process on the real line. The approach used is via wavelet expansions. Estimators of the intensity are proposed and their properties are studied, including the case of thresholded versions. Properties of the estimators for non-homogeneous Poisson processes follow as special cases. An application is giv...

In this paper, we study a method to construct a multivariate counting process with positive dependencies between the event occurrences. Conditional on a random effect with a positive stable distribution, the univariate counting processes are independent non-homogeneous Poisson processes with a power intensity function. The applicability of the model is illustrated in three examples: a horse rac...

The non-homogeneous Poisson process provides a generalised framework for the modelling of random point data by allowing intensity generation to vary across its domain interest (time or space). use processes have arisen in many areas signal processing and machine learning, but application is still largely limited intractable likelihood function lack computationally efficient inference schemes, a...

In this paper, using Poisson stochastic processes, we introduce an approach to the problem of the approximate join operation in random databases. It is shown that the cardinalities of the result sets obtained from an approximate join operation follow this type of stochastic process. Initially, we treat the case of the approximate join between two random tables, which is accomplished using a hom...