Recent advances in probability models have not only expanded our theoretical understanding but also opened up new avenues for addressing real-world challenges across a wide range of domains, making them an indispensable tool in modern data analysis and decision-making processes.  The probability models not only offer powerful tools for handling complex, high-dimensional data but also enable uncertainty quantification crucial for robust decision-making in various real-world problems.  

This invited paper session incorporates topics on recent developments in probability models and their applications, which include the estimation of parameters in the Olkin-Trikalinos and the Ghosh-BK models using a Bayesian approach, deriving new class of stochastic volatility models with Markov dependent errors that are useful in financial time series with detailed analysis when the errors are generated by a first order product autoregressive model, a study on the modification of the Discrete Weibull geometric (DWG) regression model known as the zero-inflated DWG (ZIDWG) regression to model the count data sets with excess zeros, and a study on the multivariate extension of the Leimkuhler Curve and associated Gini index and their applications in the analysis and comparison of concentration of bibliometric measures of productivity from different sources.