Features Extraction via Bayesian Analyses Cum Mixture Probabilistic Models by Extensive Computations

• Activity Number: 204 - Statistical Computing by Deep Learning and Penalization
• Type: Contributed
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Humayun Kiser* and Mian Arif Shams Adnan
• Companies: Comilla University and Bowling Green State University
• Keywords: Generalized Linear Models; Prior Probability; Posterior Distribution

Abstract

Adnan et al (2021, 2020, 2015, 2013, 2012, 2011, 2010, 2009) developed many mixture distributions along with their properties. The mixture distributions include Power function, Two Folded Mixture, Folded Gamma, Triple mixture, Laplace mixture, Pareto mixture, F mixture, dual mixture, Beta mixture, Weibull mixture, etc. The theoretical features were derived for each of these distributions. The congruence of these features has been checked computationally by the virtue of Bayesian Analysis. Several statistical features of the probabilistic models have been checked whether congruent to those obtained by statistical computations. R programming has been used to mimic the properties and to estimate the parameters.