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Marek Druzdzel is a Founding Partner of BayesFusion, LLC. and a professor emeritus at the School of Computing and Information, University of Pittsburgh. He is a graduate of the Delft University of Technology, The Netherlands with a M.Sc. degrees in Computer Science and in Electrical Engineering, and holds a Ph.D. degree from Carnegie Mellon University, Pittsburgh, PA, USA. His research focuses on building decision support systems that are based on sound principles of probability theory, statistics, and econometric. He has worked in the area of decision-theoretic systems for almost 40 years.

In 1995, Prof. Marek J. Druzdzel created the Decision Systems Laboratory, at the University of Pittsburgh. The research group focused on research and training in decision-analytic approaches to decision support. From the very beginning, he decided that the laboratory would be developing its own decision modeling software and it would make it available to the community. In June 2015, Marek Druzdzel and his colleague Tomek Sowinski created BayesFusion, LLC, and obtained an exclusive license for GeNIe and SMILE from the University of Pittsburgh.

She studied law, history and political science at various Russian higher educational institutions (1984-1994), received a doctorate in philosophy in 1995, and was enrolled in the Faculty of Basic and Integrative Sciences of the University of Vienna. Fellow at FWF (Lise Meitner Postdoctoral Fellowship), Ludwig Boltzmann Institute for the Consequences of Wars. She worked at the Austrian State Archives (Commission of Historians) and the Institute for the Danube Region and Central Europe/Vienna (IDM). A series of lectures and seminars at the Diplomatic Academy of Vienna, at the Institute for the History of Eastern Europe of the University of Vienna, as well as at the Institute of Political Science of the University of Vienna.

She is currently vice-president and manager of the International Center for Eastern Europe Research (ICEUR).
Publications on Russia's foreign and security policy, Austrian and international politics, political Islam and ethno-political conflicts in the post-Soviet space.

Изучала право, историю и политологию в различных российских высших учебных заведениях (1984-1994), получила степень доктора философских наук в 1995 году, зачислена на факультет фундаментальных и интегративных наук Венского университета. Научный сотрудник FWF (Lise Meitner Postdoctoral Fellowship), институт последствий войн Людвига Больцмана. Работала в Австрийском государственном архиве (комиссия историков) и институте Дунайского региона и Центральной Европы/ Вена (IDM). Цикл лекций и семинаров в Дипломатической академии Вены, в Институте истории Восточной Европы Венского университета, а также в Институте политических наук Венского университета.

В настоящее время научный директор Международного центра исследований Восточной Европы в Вене (ICEUR). Публикации по внешней политике и политике безопасности России, австрийской и международной политике, политическому исламу и этнополитическим конфликтам на постсоветском пространстве.

Экспозе будет опубликовано в ближайшее время

Completed studies in law, political science, and foreign languages. Lecturer at Vienna-based teaching centers affiliated with U.S. universities. Held a chair of political science at the University of Vienna.

He teaches Bayesian reasoning and the application of GeNIe 5.0 and QGeNIe to political forecasting, modeling uncertainty, and decision-making under uncertainty.

Visiting professorships and guest lectures in various countries (Russia, Hungary, Poland, Iraq, Egypt, Cambodia). Worked in various field missions and presences of the international organization OSCE (Tbilisi, Chechnya, Belgrade). Co-founder of ICEUR-Vienna and currently its Vice President.

Author of publications on Soviet, Russian, and Eastern European politics in various languages. Able to communicate in 12 languages.

Bayesian methods provide a practical framework for reasoning and decision-making in professional environments where information is incomplete, uncertain, or continuously changing. This lecture explores how Bayesian models can be applied across different fields to combine available data, expert knowledge, and assumptions about relationships between relevant factors.

Through examples from risk assessment, policy and political analysis, business and strategic decision-making, engineering, and scientific assessment, participants will examine how different professional questions can be translated into probabilistic models. Particular attention will be given to identifying relevant variables, representing dependencies, incorporating new evidence, and interpreting changes in probabilities as information becomes available.

The lecture will also consider how the same underlying Bayesian principles can be adapted to very different decision-making contexts, demonstrating how Bayesian networks can serve not only as forecasting tools, but also as a structured way to analyse uncertainty, compare scenarios, test assumptions, and support decisions under uncertainty.

This lecture deals with frequent modeling pitfalls and helpful workarounds. It will also cover QGenie, dynamic models and function nodes.
Hands-on Exercises.

This lecture has the objective to try to drive home some messages made in earlier lectures and discuss data problems as well as limits of forecasting and analysis

his hands-on session focuses on the practical construction, analysis, and interpretation of Bayesian network models using GeNIe 5.0. Participants will work directly with the software, translating real-world problems into structured probabilistic models and exploring how assumptions, dependencies, and evidence affect model outcomes.

The exercises will cover the key stages of model development: defining variables and states, creating dependencies between nodes, specifying probabilities, entering evidence, and analysing posterior probabilities. Participants will experiment with different scenarios and observe how new information propagates through a Bayesian network and changes its conclusions.

Particular attention will be given to the interpretation of model results and practical model refinement. By the end of the session, participants will have worked through the complete process of building and using a Bayesian network in GeNIe 5.0 as a tool for structured analysis and decision-making under uncertainty.

Экспозе будет опубликовано в ближайшее время

Dr. Ullrika Sahlin is a Senior Lecturer at the Department of Earth and Environmental Sciences at Lund University. Her research focuses on the characterisation, assessment, and communication of uncertainty in scientific models and assessments, and on how uncertainty can be incorporated into decision-making processes.

She has particular expertise in Bayesian methods, uncertainty analysis, expert judgement, and robust decision-making. Sahlin has contributed to a broad range of application areas, including environmental risk assessment, climate-related decision support, invasive species management, ecosystem services, toxicology, and evidence-based policy.

Her publications include methodological developments in Bayesian analysis, expert knowledge elicitation, uncertainty quantification, and model-based assessment. She has authored and co-authored more than 60 research outputs, including articles, reports, book chapters, and conference papers.

This lecture examines the different types and sources of uncertainty encountered in Bayesian network (BN) modelling. It begins by distinguishing between BNs used to represent uncertainty about unique events and those describing sampling events, and then explores uncertainty arising from limited data through simple examples involving categorical outcomes and diagnostic testing.

These examples illustrate the effects of sparse evidence, the interpretation of probabilities for unique versus repeatable events, and the importance of Cromwell’s rule: probabilities should not be assigned a value of zero unless a proposition is logically impossible.

The lecture then considers uncertainty related to model structure, network structure, and expert judgement. Examples demonstrate how assumptions about dependencies between variables can affect inference, and how alternative network structures can be compared using likelihood-based methods commonly employed in machine learning. Approaches for reducing bias in expert elicitation are also discussed. Finally, the lecture presents a broader view of Bayesian networks, arguing that any Bayesian model can be represented as a BN, while the term is often used more narrowly for networks with categorical or discretised variables. A simple example of a BN with continuous variables is used to illustrate this perspective.

Dr. Trefor Bazett is an Associate Teaching Professor in the Department of Mathematics and Statistics at the University of Victoria. Trefor is a passionate educator and has won nine teaching awards in his career.

Trefor also shares his love of math on social media. With over 600,000 subscribers on YouTube alone, his videos have supported millions of students learning math online. By sharing some of the coolest stories in math, Trefor’s videos help people discover and sustain an appreciation for mathematics.

Trefor received his PhD from the University of Toronto in an area of mathematics called Algebraic Topology which investigates how to do algebraic computations on strange mathematical shapes. He has previously taught at the University of Oxford, University of Cincinnati, and University of Toronto.

This lecture is an introduction to Bayesian Reasoning. We will discuss how Bayesian reasoning provides a framework for making and refining predictions in uncertain situations. We will begin by introducing the core ideas of prior and posterior beliefs and conditional probability.

We will then explore Bayes' Formula and it's consequences. Along the way we will see plenty of practical examples and explore how Bayesian reasoning can inform our understanding of the world.

Ilija Vorontsov is a software engineer and Team Lead at LOYTEC Electronics in Vienna, specializing in embedded systems and software development. His professional experience includes work with Zephyr RTOS, Bluetooth LE and Bluetooth Mesh, embedded Linux, hardware design, and software testing and verification.

He studied Computer Engineering at TU Wien, with a focus on automation and control theory, software validation and verification, dependable and distributed systems, FPGA and microcontroller programming, and SAT solving. His academic work includes real-time performance analysis on ARM Cortex microcontrollers and proof transformation for SAT solver verification.

Since 2025, Ilija has taught probabilistic modeling at ICEUR and is ICEUR’s leading specialist in practical modeling with GeNIe 5.0. His teaching covers Bayes’ theorem, Bayesian networks, and the development, analysis, and interpretation of probabilistic models using GeNIe 5.0.

This lecture focuses on the development and interpretation of Bayesian network models, from translating a real-world problem into a network structure to understanding the probabilistic conclusions produced by the model.

Participants will learn how to identify relevant variables and their possible states, define dependencies between them, and specify conditional probabilities. The lecture will examine how evidence is introduced into a Bayesian network, how it propagates through the model, and how posterior probabilities change as new information becomes available.

Particular attention will be given to interpreting model results, exploring alternative scenarios, and understanding how assumptions about dependencies and probabilities influence conclusions. The session will provide participants with a practical foundation for developing Bayesian network models and using them for structured analysis, forecasting, and decision-making under uncertainty.

Лекция посвящена разработке и интерпретации моделей байесовских сетей — от преобразования реальной задачи в структуру сети до понимания вероятностных выводов, получаемых с помощью модели.

Участники научатся определять значимые переменные и их возможные состояния, устанавливать зависимости между ними и задавать условные вероятности. Будет рассмотрено, как новые данные вводятся в байесовскую сеть, как они распространяются по модели и как меняются апостериорные вероятности по мере поступления новой информации.

Особое внимание будет уделено интерпретации результатов моделирования, анализу альтернативных сценариев и пониманию того, как исходные предположения о зависимостях и вероятностях влияют на выводы. Лекция даст участникам практическую основу для разработки моделей байесовских сетей и их применения в структурированном анализе, прогнозировании и принятии решений в условиях неопределённости.