Important Dates
Early Bird Registration :30th November 2026
Paper Submission Deadline :5th December 2026
Last Date of Registration : 15th December 2026
Date of Conference : 30th - 31st December 2026
Conference Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals.
SDG 3 — Good Health and Well-being
SDG 4 — Quality Education
SDG 9 — Industry, Innovation and Infrastructure
SDG 11 — Sustainable Cities and Communities
Track 01
Advancements in Bayesian Modeling Techniques
This track focuses on the latest developments in Bayesian modeling methodologies, emphasizing novel approaches and theoretical advancements. Researchers are encouraged to present innovative techniques that enhance the flexibility and applicability of Bayesian models.
Track 02
Statistical Inference in Complex Data Structures
This session aims to explore statistical inference methods tailored for complex data structures, including hierarchical and multilevel models. Contributions that address the challenges of inference in high-dimensional and structured data are particularly welcome.
Track 03
Machine Learning and Bayesian Approaches
This track investigates the intersection of machine learning and Bayesian inference, highlighting how Bayesian methods can enhance learning algorithms. Topics may include Bayesian neural networks, probabilistic graphical models, and uncertainty quantification in machine learning.
Track 04
Predictive Modeling with Bayesian Frameworks
This session is dedicated to the application of Bayesian frameworks in predictive modeling across various domains. Papers that demonstrate the effectiveness of Bayesian methods in improving prediction accuracy and model interpretability are encouraged.
Track 05
Markov Chains and Monte Carlo Methods
This track delves into the theoretical and practical aspects of Markov chains and Monte Carlo methods in Bayesian statistics. Contributions that explore new algorithms, convergence properties, and applications in complex models are sought.
Track 06
Bayesian Networks and Graphical Models
This session focuses on the development and application of Bayesian networks and other graphical models for statistical inference. Researchers are invited to present work that advances the understanding of dependencies and causal relationships in data.
Track 07
Prior Distributions and Posterior Estimation
This track examines the role of prior distributions in Bayesian analysis and their impact on posterior estimation. Papers that propose new priors, discuss prior sensitivity, or explore empirical Bayes methods are particularly relevant.
Track 08
Computational Statistics and Bayesian Inference
This session highlights computational techniques that facilitate Bayesian inference, including algorithms for high-dimensional data and large-scale models. Contributions that address computational challenges and improve efficiency in Bayesian analysis are encouraged.
Track 09
Quantitative Methods in Bayesian Research
This track focuses on quantitative methods that enhance Bayesian research, including statistical techniques and data analysis strategies. Papers that showcase innovative applications of quantitative methods in various fields are welcome.
Track 10
Applications of Bayesian Inference in Real-World Problems
This session seeks to highlight the practical applications of Bayesian inference across diverse fields such as healthcare, finance, and environmental science. Researchers are invited to share case studies and empirical research that demonstrate the utility of Bayesian methods.
Track 11
Emerging Trends in Bayesian Data Science
This track explores emerging trends and future directions in Bayesian data science, including the integration of artificial intelligence and big data analytics. Contributions that discuss innovative applications and theoretical advancements in this rapidly evolving field are encouraged.