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Dr. Marek J. Druzdzel is a leading expert in Bayesian networks, probabilistic reasoning, and decision support systems, Professor at Białystok University of Technology and Professor Emeritus at the University of Pittsburgh.

For nearly four decades, his research has focused on decision-making under uncertainty, probabilistic inference, and intelligent decision support systems based on probability theory, statistics, and decision theory. A major part of his work has been devoted to Bayesian networks, ranging from their theoretical foundations and inference algorithms to their practical application in modelling complex real-world systems.

Prof. Druzdzel is the principal conceptual designer of GeNIe and SMILE, software tools for building Bayesian networks and graphical decision models. These tools have been developed over decades of academic research and are now used internationally in research, education, and applied decision modelling.

In 1995, he founded the Decision Systems Laboratory at the University of Pittsburgh, which he directed until 2019. The laboratory conducted research on decision analysis, probabilistic modelling, and intelligent decision support systems and played a central role in the development of GeNIe and SMILE.

In 2015, Prof. Druzdzel co-founded BayesFusion, continuing the development of GeNIe and SMILE and bringing Bayesian modelling and decision-support technology to a broad range of practical applications.

He received his PhD in Engineering and Public Policy from Carnegie Mellon University, where his research focused on probabilistic reasoning in decision support systems. His academic background also includes two Master’s degrees from Delft University of Technology and a habilitation from the Institute of Computer Science of the Polish Academy of Sciences.

His research and publications span Bayesian networks, causal reasoning, probabilistic inference, decision theory, knowledge engineering, and decision-making under uncertainty, with applications ranging from medical decision support and diagnosis to risk analysis and complex systems modelling.

Dr. Ludmilla Lobova is a political scientist, Scientific Director and Vice President of ICEUR-Vienna, specializing in political forecasting, international affairs, and the analysis of political processes under uncertainty.

A central focus of her work at ICEUR is the methodology of political forecasting. She examines the limitations of conventional expert analysis, the sources of systematic forecasting errors, and the ways in which probabilistic and Bayesian reasoning can provide a more rigorous framework for dealing with uncertainty. Rather than seeking a single “correct” prediction, this approach emphasizes assigning probabilities to possible outcomes, updating assessments as new information becomes available, and systematically learning from forecasting errors.

Her academic and analytical work also covers international relations, Russian foreign and security policy, political conflicts, and transformation processes across the post-Soviet space.

Dr. Lobova studied law, history, and political science and received her doctoral degree in 1995. In Austria, she was a Lise Meitner Fellow of the Austrian Science Fund (FWF), conducting research on Russian perceptions of Austrian neutrality and its significance for European security.

She has worked with the Ludwig Boltzmann Institute for Research on the Consequences of War, the Austrian State Archives, and the Institute for the Danube Region and Central Europe (IDM). She has also delivered lectures and seminars at the Diplomatic Academy of Vienna and the University of Vienna.

Dr. Lobova is the author of publications on Russian foreign and security policy, European security, political Islam, and ethno-political conflicts. Together with Prof. Hans-Georg Heinrich, she has also served as an editor of the academic series ICEUR Insight Studies.

At the ICEUR School of Political Forecasting, she combines decades of experience in political and regional analysis with Bayesian reasoning, scenario analysis, and probabilistic forecasting, emphasizing an approach in which the quality of a forecast depends not on an expert’s confidence, but on the ability to formulate testable assessments, account explicitly for uncertainty, and revise conclusions as new evidence emerges.

Эта вводная лекция открывает курс ICEUR, посвящённый политическому прогнозированию и анализу нового миропорядка, сформировавшегося в ходе войны в Украине. Лекция объясняет, почему привычные способы понимания мировой политики — линейные прогнозы, интуитивные оценки и экспертные «уверенные заявления» — всё чаще дают сбой. Мир вступил в фазу высокой неопределённости, где ошибки прогнозирования становятся системными, а не случайными.

Особое внимание уделяется различию между прогнозированием, предсказанием и пророчеством, а также причинам, по которым прогнозам не доверяют — от когнитивных искажений до неправильного понимания вероятности. На исторических примерах показывается, почему одни прогнозы оказывались успешными (распад СССР), а другие — провальными.

Вторая часть лекции вводит слушателей в байесовское мышление как основу работы с неопределённостью. Вместо поиска «точных дат» и «окончательных ответов» предлагается подход, основанный на обновлении знаний, вероятностной логике и обучении на ошибках — с целью быть менее неправыми, а не абсолютно правыми.

Лекция также задаёт рамку всего курса: от теоретических оснований к практическому моделированию, сценарному анализу и работе с реальными политическими кейсами.

Prof. Hans-Georg Heinrich is a political scientist, political forecasting expert, and co-founder of ICEUR (Institute for the Comparative Study of European and Eurasian Regions) in Vienna.

His academic and professional career spans several decades of research into political systems, international relations, conflicts, and political transformation, with a particular focus on Eastern Europe, Russia, and the post-Soviet space. A central element of his work is the structured analysis of complex political processes and the development of methods for forecasting under conditions of uncertainty.

At ICEUR, Prof. Heinrich has played a key role in developing and teaching approaches to political forecasting based on probabilistic reasoning and Bayesian networks. His methodology combines expert political analysis with formal modelling techniques, transforming complex relationships between political, economic, and social factors into structured models and scenarios.
Prof. Heinrich previously held a professorship at the University of Vienna and has held visiting teaching and research positions at universities and academic institutions in Russia, Hungary, Poland, Iraq, Egypt, and other countries.

Alongside his academic career, he has extensive experience working in international environments, including assignments with the OSCE in Tbilisi, Chechnya, and Belgrade. This combination of academic research and field experience has informed his approach to analysing political instability, conflict, and complex international developments.

He is the author and editor of numerous academic publications on Soviet and Russian politics, Eastern Europe, political transformation, conflict, and international relations.

At ICEUR, Prof. Heinrich brings together decades of experience in comparative politics and international affairs with Bayesian modelling and structured political forecasting, applying these methods to the analysis of complex political scenarios and the systematic assessment of possible future developments.

In this lecture, Professor Marek Druzdzel will demonstrate how Bayesian networks can be used for diagnostic problems where the goal is to identify the most likely cause of an observed condition based on incomplete or uncertain information.

The lecture will explore how a diagnostic model connects possible causes with observable signs, symptoms, and test results, and how new information changes the probabilities of different explanations. Participants will be introduced to the principles of diagnostic inference in Bayesian networks and see how a model can be used to progressively refine initial hypotheses.

Particular attention will be given to selecting the most informative observations and tests. Through examples, the lecture will demonstrate how to assess which additional information can reduce uncertainty most effectively and help determine the next step in the diagnostic process.
The lecture will illustrate how this approach can be applied to different types of problems — from technical and medical diagnosis to more general situations in which the task is to identify the likely causes behind an observed outcome.

Лекция будет вестись на английском языке

В этой лекции профессор Марек Друдзель покажет, как байесовские сети могут использоваться для решения задач диагностики, где необходимо определить наиболее вероятную причину наблюдаемого состояния на основе неполной или неопределённой информации.

Будет рассмотрено, как диагностическая модель связывает возможные причины с наблюдаемыми признаками, симптомами и результатами тестов и как поступление новой информации изменяет вероятность различных объяснений. Участники познакомятся с принципами диагностического вывода в байесовских сетях и увидят, как модель позволяет последовательно уточнять первоначальные гипотезы.

Особое внимание будет уделено выбору наиболее информативных наблюдений и тестов. На примерах будет показано, как оценивать, какая дополнительная информация способна сильнее всего снизить неопределённость и помочь выбрать следующий шаг в процессе диагностики.
Лекция продемонстрирует применение этого подхода к различным типам задач — от технической и медицинской диагностики до более общих ситуаций, в которых необходимо установить вероятные причины наблюдаемого результата.

Лекция будет вестись на английском языке

В этой лекции профессор Марек Друдзель рассмотрит один из ключевых этапов работы с байесовскими сетями — проверку того, насколько корректно построенная модель отражает заложенные в неё знания, зависимости и предположения.

Участники познакомятся с основными подходами к верификации структуры и параметров модели. Будет показано, как выявлять логические несоответствия, неожиданные зависимости и результаты, которые могут указывать на ошибки при построении сети или задании вероятностей.

Особое внимание будет уделено анализу поведения модели в различных сценариях. Изменяя исходные данные и вводя новые свидетельства, можно проследить, соответствуют ли изменения вероятностей ожидаемой логике и насколько устойчивы выводы модели к отдельным предположениям и параметрам.
Лекция покажет, что верификация — это не только поиск технических ошибок, но и важная часть процесса моделирования, позволяющая лучше понять построенную сеть, критически оценить её поведение и повысить обоснованность получаемых с её помощью выводов.

In this lecture, Professor Marek Druzdzel will address one of the key stages in working with Bayesian networks — verifying whether a model correctly reflects the knowledge, dependencies, and assumptions on which it was built.

Participants will be introduced to the main approaches to verifying a model’s structure and parameters. The lecture will show how to identify logical inconsistencies, unexpected dependencies, and results that may indicate errors in the network structure or in the specification of probabilities.

Particular attention will be given to examining model behavior under different scenarios. By changing inputs and introducing new evidence, participants will see how to determine whether changes in probabilities follow the expected logic and how sensitive the model’s conclusions are to particular assumptions and parameters.
The lecture will demonstrate that model verification is not simply about finding technical errors. It is an essential part of the modeling process that helps analysts better understand their models, critically assess their behavior, and increase confidence in the conclusions derived from them.

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.

Практическое занятие посвящено построению, анализу и интерпретации моделей байесовских сетей с использованием GeNIe 5.0. Участники будут работать непосредственно с программой, преобразуя реальные задачи в структурированные вероятностные модели и исследуя, как исходные предположения, зависимости и новые данные влияют на результаты моделирования.

Упражнения охватывают основные этапы разработки модели: определение переменных и их состояний, создание зависимостей между узлами, задание вероятностей, ввод свидетельств и анализ апостериорных вероятностей. Участники смогут рассмотреть различные сценарии и проследить, как новая информация распространяется по байесовской сети и изменяет получаемые выводы.

Особое внимание будет уделено интерпретации результатов и практической доработке моделей.

К концу занятия участники пройдут полный цикл построения и использования байесовской сети в GeNIe 5.0 как инструмента структурированного анализа и принятия решений в условиях неопределенности.

Dr. Ullrika Sahlin is an Associate Professor at Lund University, Sweden, specializing in Bayesian modelling, risk analysis, uncertainty quantification, and decision-making under uncertainty.

A central focus of her research is how uncertainty can be represented, quantified, and communicated in scientific assessments and complex decision-making. Her work draws on Bayesian analysis, robust Bayesian methods, expert knowledge elicitation, and evidence synthesis to support reasoning and decision-making when information is incomplete, uncertain, or based on multiple sources.

A particularly important area of her research is the use of Bayesian networks and predictive models. She has studied how Bayesian networks can integrate empirical data, scientific models, and expert judgement while explicitly accounting for different sources of uncertainty. Her research on epistemic uncertainty in Bayesian networks demonstrates how these models can be used for risk assessment and for reasoning about uncertain future events.

Dr. Sahlin has extensive experience working at the interface between academic research and applied risk assessment. She has contributed to the work of the European Food Safety Authority (EFSA) on uncertainty analysis and has led EFSA-related projects focused on the development and application of Expert Knowledge Elicitation methods.

Her interdisciplinary academic background combines mathematical statistics with applied risk and ecological modelling. She holds a Master’s degree in Mathematical Statistics from Lund University, a Master’s degree in Forestry from the Swedish University of Agricultural Sciences, and a PhD in Ecology, where her research focused on risk assessment related to invasive species.

Her current research interests include robust Bayesian analysis, evidence synthesis, expert judgement, risk analysis, and decision-making under uncertainty. She also teaches doctoral-level courses in Bayesian analysis and decision theory, as well as risk, uncertainty, and decision-making.

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 a mathematician and lecturer in the Department of Mathematics and Statistics at the University of Victoria, Canada, with expertise in mathematical education and a strong focus on making advanced mathematical concepts accessible and intuitive.

A significant part of his educational work covers probability theory, conditional probability, Bayes’ theorem, and Bayesian inference — the mathematical foundations for reasoning under uncertainty and updating probabilistic assessments as new information becomes available. His teaching demonstrates how Bayesian reasoning provides a rigorous framework for moving from prior assumptions to updated conclusions based on new evidence.

Dr. Bazett is the author of an educational series on Bayes’ theorem and Bayesian inference, published by Springer Nature. The series explores conditional probability, prior and posterior probabilities, different formulations of Bayes’ theorem, false positives, and the practical interpretation of probabilistic evidence.

Known for his ability to explain complex mathematical ideas clearly and intuitively, Dr. Bazett has created more than 500 educational mathematics videos reaching an international audience. His online mathematics courses and lectures have attracted hundreds of thousands of learners worldwide.

He received his PhD in Mathematics from the University of Toronto and has taught at the University of Toronto and the University of Cincinnati before joining the University of Victoria. His contributions to mathematics education have been recognized with major teaching distinctions, including the PIMS Education Prize (2024) and the University of Victoria Faculty of Science Award for Teaching Excellence (2025).

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.

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.

SELF-PACED · €149

Learn at your own pace - with guidance when you need it.

The Self-Paced format gives you the complete course without requiring you to follow a fixed class schedule.
But this is more than access to recorded lectures. You follow a structured learning path, check your understanding after each session, work with GeNIe 5.0, build your own Bayesian Network model, and can ask the instructors questions along the way.

HOW IT WORKS
01 · Your course starts on October 12
Beginning October 12, 2026, two new recorded sessions will be released each week, together with the corresponding course materials, exercises and knowledge checks.
Once a session has been released, it remains available to you. There are no fixed viewing times — watch when it suits you, revisit difficult topics and repeat exercises whenever necessary.
Your course access remains active for 12 months.

02 · Check your understanding after each session
Each lecture is followed by a set of multiple-choice questions designed to check your understanding of the key concepts.
They help you identify what you have understood well — and which topics may be worth revisiting before moving forward.

03 · Work hands-on with GeNIe 5.0
This course is not only about understanding Bayesian Networks. You will actually build and work with them.
You will complete practical exercises in GeNIe 5.0, explore existing models and learn how variables, dependencies, probabilities and new evidence affect model outcomes.
A GeNIe 5.0 academic license is included.

04 · Build your own Bayesian Network
As your final practical project, you will create your own model in GeNIe 5.0.
You can choose a problem relevant to your professional or research interests — from business, medicine and risk management to political analysis, security, engineering or another field.
The objective is to demonstrate that you can turn a real-world problem into a structured probabilistic model and interpret its results.

05 · Ask the instructors
Self-paced does not mean unsupported.
During the course, you can submit written questions about the course material, practical exercises or your model to Prof. Hans-Georg Heinrich and Ilja Vorontsov.
So while you determine your own learning schedule, expert guidance remains available when you need it.

06 · Present and defend your model
After completing the course, you can submit your own GeNIe model and present the reasoning behind it.
You should be able to explain the structure of your model, the assumptions you made, the relationships between variables and how its results should be interpreted.
The goal is not simply to produce a working file, but to demonstrate that you understand the logic behind the model you have built.

CERTIFICATE
A certificate is not issued simply for watching the recordings.
To receive your ICEUR Certificate, you will need to complete the required knowledge checks and successfully present your own GeNIe 5.0 model.
This means the certificate reflects actual completion of the course and demonstrated practical understanding.

INCLUDED IN SELF-PACED
✓ Complete recorded course
✓ Two new recorded sessions released each week from October 12
✓ Knowledge checks after each session
✓ Hands-on exercises
✓ Complete course materials
✓ GeNIe 5.0 academic license
✓ Model files and templates
✓ Your own final Bayesian Network project
✓ Written questions to Prof. Heinrich and Ilya Vorontsov
✓ Opportunity to earn an ICEUR Certificate
✓ 12 months of access

LIVE COHORT · €590

Learn with the instructors - and become part of the cohort.

The Live Cohort gives you the complete course in an interactive format, combining structured learning with live sessions, hands-on practice, direct access to the instructors and an active learning community.
You follow the same core curriculum, but you do not go through it alone. You can ask questions as they arise, discuss your models and ideas, work through problems together with the instructors, and stay connected between sessions through a private Telegram group.

HOW IT WORKS
01 · Join six live interactive sessions
The course includes six live online sessions with the instructors.
Rather than simply watching a finished presentation, you can follow the reasoning as it develops, ask questions in real time and take part in discussions.
The instructors demonstrate models, explain their decisions and work through practical problems together with the group.

02 · Work hands-on with the instructors
Practical exercises are an integral part of the live course.
You will work with GeNIe 5.0, learning how to define variables and states, build dependencies, specify probabilities, introduce evidence and interpret the resulting changes in the model.
You can discuss alternative approaches, ask why a model behaves in a particular way and work through difficulties directly with the instructors.
A GeNIe 5.0 academic license is included.

03 · Stay connected in your private Telegram group
Your interaction with the course does not end when a live session finishes.
Live Cohort participants receive access to a private Telegram group dedicated to the course.
There you can ask questions directly to ICEUR School or any of the course instructors, discuss lectures and exercises, share questions about your own model, and communicate with other members of the cohort.
If a question comes up between sessions, you do not have to wait until the next class to raise it.

04 · Build your own Bayesian Network
As part of the course, you will develop your own model in GeNIe 5.0.
You can choose a problem relevant to your professional, academic or personal interests — whether in business, medicine, risk management, research, political analysis, security, engineering or another field.
In the Live Cohort, you can discuss your ideas and difficulties with the instructors as your model develops.

05 · Present and defend your model
Your own Bayesian Network becomes the final practical project of the course.
You will present the model and explain the reasoning behind it: why you structured the problem in a particular way, how the variables are related, what assumptions you made and how the results should be interpreted.
This gives you the opportunity not only to build a model, but also to discuss and defend your approach with the instructors.

06 · Keep the complete course for 12 months
Live learning does not mean you have only one opportunity to understand the material.
Recordings of the sessions, course materials, exercises, model files and templates remain available to you for 12 months.
You can return to individual lectures, repeat exercises and revisit your own model long after the live course has finished.

CERTIFICATE
After successfully completing the course requirements and presenting your final model, you will receive an ICEUR Certificate.
The certificate reflects both your understanding of the methodology and your ability to apply it in practice by developing and explaining your own Bayesian Network model.

INCLUDED IN LIVE COHORT
✓ 6 live interactive online sessions
✓ Hands-on work with the instructors
✓ Questions and discussion in real time
✓ Private Telegram course group
✓ Direct access to ICEUR School and all course instructors
✓ Practical exercises in GeNIe 5.0
✓ Complete course materials
✓ GeNIe 5.0 academic license
✓ Model files and templates
✓ Your own final Bayesian Network project
✓ Presentation and discussion of your model
✓ ICEUR Certificate upon successful completion
✓ 12 months of access to recordings