International Conference on Bayesian Networks and Decision Analysis

ICBNDA

26th Sep – 27th Sep 2026 Istanbul, Turkey

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Registration Summary

Conference ICBNDA

Date 26th Sep – 27th Sep 2026

Location Istanbul, Turkey

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

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Conference Session Tracks

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

01 Foundations of Bayesian Networks +

This track focuses on the theoretical underpinnings of Bayesian networks, exploring their mathematical foundations and structural properties. Participants will discuss advancements in probabilistic reasoning and the implications for decision-making processes.

SDG 4 SDG 9

02 Statistical Modeling Techniques +

This session will delve into various statistical modeling techniques that leverage Bayesian frameworks for enhanced inference. Emphasis will be placed on model selection, validation, and the integration of prior knowledge.

SDG 9 SDG 11

03 Bayesian Inference in Practice +

This track will cover practical applications of Bayesian inference across diverse fields, highlighting case studies and real-world implementations. Participants will share insights on computational challenges and solutions in Bayesian analysis.

SDG 3 SDG 9

04 Machine Learning and Bayesian Methods +

This session explores the intersection of machine learning and Bayesian methodologies, focusing on how Bayesian principles can enhance predictive modeling. Discussions will include algorithmic advancements and their applications in artificial intelligence.

SDG 4 SDG 9

05 Risk Analysis and Decision Support +

This track will address the role of Bayesian networks in risk analysis and decision support systems. Participants will examine frameworks for quantifying uncertainty and making informed decisions under risk.

SDG 3 SDG 16

06 Simulation Techniques in Bayesian Analysis +

This session will focus on simulation methods such as Markov Chain Monte Carlo (MCMC) and their applications in Bayesian analysis. Participants will discuss innovations in simulation techniques that improve computational efficiency.

SDG 9

07 Data Science and Bayesian Approaches +

This track will explore the integration of Bayesian approaches within the data science paradigm, emphasizing data-driven decision-making. Discussions will include the role of Bayesian statistics in handling large datasets and complex models.

SDG 4 SDG 9

08 Predictive Analytics Using Bayesian Networks +

This session will highlight the use of Bayesian networks for predictive analytics, showcasing methodologies for forecasting and trend analysis. Participants will share best practices for implementing Bayesian models in predictive tasks.

SDG 9 SDG 11

09 Optimization Techniques in Bayesian Inference +

This track will delve into optimization techniques that enhance Bayesian inference processes, focusing on parameter estimation and model fitting. Participants will discuss the trade-offs between computational complexity and model accuracy.

SDG 9

10 Applied Statistics in Bayesian Frameworks +

This session will examine the application of Bayesian statistics across various domains, including healthcare, finance, and social sciences. Participants will present case studies that demonstrate the effectiveness of Bayesian methods in real-world scenarios.

SDG 3 SDG 9

11 Algorithms for Bayesian Decision Making +

This track will focus on algorithmic developments that facilitate Bayesian decision-making processes, including advancements in computational algorithms and heuristics. Participants will discuss the implications of these algorithms for real-time decision support.

SDG 4 SDG 9