This track focuses on the latest advancements in Bayesian methodologies and their applications in various fields. Researchers are encouraged to present novel approaches to Bayesian inference, model selection, and computational techniques.

This session will explore statistical inference methods tailored for high-dimensional data settings. Topics may include variable selection, dimensionality reduction, and the challenges of overfitting in complex models.

This track aims to delve into the theory and applications of random processes across different domains. Contributions may include stochastic modeling, time series analysis, and applications in finance and engineering.

This session will highlight innovative computational techniques and simulation methods used in statistical analysis. Participants are invited to share advancements in Monte Carlo methods, bootstrapping, and other resampling techniques.

This track will bridge the gap between traditional statistical methods and modern machine learning techniques. Presentations may focus on the integration of statistical theory with machine learning algorithms for improved predictive performance.

This session will cover the intersection of data science and statistical methodologies for predictive analytics. Topics of interest include data-driven decision-making, model evaluation, and the role of big data in statistical inference.