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Call For Papers

The ICBMIS bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Statistics, Data Science, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Bayesian modeling techniques in statistics
  • Applications of Bayesian inference in research
  • Bayesian methods for hierarchical models
  • Statistical challenges in Bayesian analysis
  • Bayesian approaches to causal inference
  • Computational methods for Bayesian statistics
  • Bayesian statistics in clinical trials
  • Machine learning and Bayesian methods integration
  • Bayesian modeling of time series data
  • Statistical software for Bayesian analysis
  • Bayesian methods for missing data
  • Bayesian networks in statistical modeling
  • Applications of Bayesian methods in epidemiology
  • Bayesian approaches to meta-analysis
  • Statistical education in Bayesian statistics
  • Future trends in Bayesian research
  • Bayesian methods for spatial data analysis
  • Bayesian statistics in environmental studies
  • Ethics in Bayesian statistical research
  • Bayesian methods for decision making

Evaluation

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Conference Alert

Due to heightened regional tensions and travel risks, the conference may be conducted in virtual-only mode. Updates regarding participation format will be communicated in advance.