This research addresses the potential for tackling crime volumes and improving crime
analytics through new enhancement strategies. The use of machine learning and deep …

The influence of social media in disseminating information, especially during the COVID-19
pandemic, can be observed with time interval, so that the probability of number of tweets …

For the hierarchical inverse gamma and inverse gamma model, we calculate the Bayes
posterior estimator of the rate parameter of the inverse gamma distribution under Stein's loss …

Recurrent event data frequently arise in practice, and in some cases, the event process has
cyclic or periodic components. We propose a semiparametric rate model with multiple event …

An optimal filter for Poisson observations is developed as a variant of the traditional Kalman
filter. Poisson distributions are characteristic of infectious diseases, which model the number …

For the hierarchical uniform and inverse gamma model, we calculate the Bayes posterior
estimator of the parameter of the uniform distribution under Stein's loss function which …

For the hierarchical gamma and gamma model, we calculate the Bayes estimator of the rate
parameter of the gamma distribution under Stein's loss function which penalizes gross …

In this paper, we develop the mathematical framework for filtering problems arising from
biophysical applications where data is collected from confocal laser scanning microscopy …

For the hierarchical normal and inverse gamma model, we calculate the Bayes posterior
estimator of the variance parameter of the normal distribution under Stein's loss function …

Many networks have event-driven dynamics (such as communication, social media and
criminal networks), where the mean rate of the events occurring at a node in the network …