Registration summary
ConferenceICBMIS
ModeStandard / Physical
ParticipationListener
Registration fee$175.00
Bank charges (5.8%)$10.15
Total payable$185.15
Includes all bank processing charges — the amount above is exactly what will be charged.
Benefits of Registering as Listener
Access to Conference Sessions
Networking Opportunities
Certificate of Participation
Invitation Letter Support
Conference Kit / Materials
Access to Keynote Sessions
• Conference Session Tracks •
SDG-Aligned Research Themes
ICBMIS conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
