A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

Density Estimation of Neonatal Mortality Rate Using Empirical Bayes Deconvolution with Poisson Distributed Surrogate in Central Java Province Indonesia

This article is concerned with the density estimation of Neonatal Mortality Rate (NMR) in Central Java Province, Indonesia. Neonatal deaths contribute to 73% of infant deaths in Central Java Province. The number of neonatal deaths for 35 districts/municipalities in Central Java Province is considered as Poisson distributed surrogate with NMR as the rate of Poisson distribution. It is assumed that each number of neonatal deaths by district/municipality in Central Java Province were realizations of unobserved NMR, which come from unknown prior density. We applied the Empirical Bayes Deconvolution (EBD) method for estimating the unknown prior density of NMR based on Poisson distributed surrogate. We used secondary data from the Health Profiles of Central Java Province, Indonesia, in 2018. The density estimation of NMR by the EBD method showed that the resulting prior estimate is relatively close to the Gamma distribution based on Poisson surrogate. This is implying that the suitability of the obtained prior density estimation as a conjugate prior for Poisson distribution.

Reliability Estimation for the Two-Parameter Exponential Family Based on Progressively Type-II Censored Data

In this paper, we deal with the problem of estimating the reliability function of the two-parameter exponential distribution. Classical Maximum likelihood and Bayes estimates for one and two parameters and the reliability function are obtained on the basis of progressively type-II censored samples. The inverted gamma conjugate prior density is assumed for the one-parameter case, whereas the joint prior density of the two-parameter case is composed of the inverted gamma and the uniform densities. A comparison between the obtained estimators is made through a Monte Carlo simulation study. A real example is used to illustrate the proposed methods.

Bayesian Prediction Bounds for the Exponential-Type Distribution Based on Ordered Ranked Set Sampling

We suggest a ranked set sample method to improve Bayesian prediction intervals. The paper deals with the Bayesian prediction intervals in the context of an ordered ranked set sample from a certain class of exponential-type distributions. A proper general prior density function is used and the predictive cumulative function is obtained in the two-sample case. The special case of linear exponential distributed observations is considered and completed with numerical results.

New developments in the BayMEM Suite

The program suite BayMEM consists of the programs PRIOR, BayMEM and EDMA. It is intended to apply the Maximum Entropy Method (MEM) to ordinary and modulated structures[1]. The PRIOR program is intended to calculate the prior density for the MEM calculation, but it has been recently shown that it can be used to calculate the dynamic charge density from multipolar refinements[2] as well. As a new functionality it is now also possible to calculate the electrostatic potential from the dynamic deformation density by the method described by Steward and Spackman[3]. We will present a new MapConverter program which allows to convert electron density stored in different file formats into an other. It is also possible to rearrange and cut the density in such a way, that it is possible, to have clear view of one molecule, not obscured by its symmetry mates, in a molecular viewer like MoleCoolQt for example.