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Probabilistic Graphical Models

Code: IM2014_13     Acronym: MGP

Classification Keyword
OFICIAL Statistics

Instance: 2020/2021 - 2S (of 08-02-2021 to 30-07-2021) Ícone do Moodle

Active? Yes
Responsible unit: Departamento de Medicina da Comunidade, Informação e Decisão em Saúde
Course/CS Responsible: Master Programme in Medical Informatics

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIM 3 Plano Oficial (Atualizado Despacho n.º 5137/2020) 1 - 3 27 81

Teaching Staff - Responsibilities

Teacher Responsibility
Pedro Pereira Rodrigues

Teaching - Hours

Theoretical and practical : 1,00
Tutorial Supervision: 0,71
Others: 0,21
Type Teacher Classes Hour
Theoretical and practical Totals 1 1,00
Pedro Pereira Rodrigues 1,00
Tutorial Supervision Totals 1 0,71
Pedro Pereira Rodrigues 0,71
Others Totals 1 0,21
Pedro Pereira Rodrigues 0,21

Teaching language



This unit aims to empower the students with necessary knowledge and skills to use modern methods of probabilistic reasoning for biomedical problems, more specifically regarding theory and practice of Bayesian networks for interdependencies exploration and clinical decision support.


Learning outcomes and competences

  • Identify different types of uncertainty inherent in clinical practice.

  • Understand the main objectives of Bayesian inference for clinical decision support.

  • Describe the theory of Bayesian networks, its objectives and the main features.

  • Observe clinical applications of Bayesian networks in different areas and outcomes.

Working method



  • Introduction

  • Motivation and examples

  • Probability and medical applciations

  • Probabilistic graphical models

  • Introduction to Bayesian networks

  • Bayesian networks

    • Semantics and factorization

    • Probabilistic influence flow

    • Conditional independence andnaive Bayes

    • Causal independence

    • Temporal Bayesian networks

  • Building Bayesian networks from data

    • Machine learning

    • Bayesian network parameter estimation

    • Bayesian network structure learning

    • Learning and inferring from incomplete data

Mandatory literature

Darwiche, A. ; Modeling and Reasoning with Bayesian Networks, Cambridge University Press, 2009
Darwiche, A. ; Bayesian networks, Communications of the ACM, 53(12), 80–90, 2010
Lucas, P. J. F., van der Gaag, L. C., & Abu-Hanna, A. ; Bayesian networks in biomedicine and health-care, Artificial Intelligence in Medicine, 30(3), 201–14., 2004
Lucas, P. ; Bayesian analysis, pattern analysis, and data mining in health care, Current Opinion in Critical Care, 10(5), 399–403., 2004
Koller, D., & Friedman, N. ; Probabilistic Graphical Models - Principles and Techniques, MIT Press., 2009
Cowell, R. G., Dawid, P., Lauritzen, S. L., & Spiegelhalter, D. J. ; Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks, Springer, 2007

Teaching methods and learning activities

Theoretical lectures and practical lessons, with topic discussion, individual and group exercises, and hands-on training with proper software

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 20,00
Trabalho laboratorial 80,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Apresentação/discussão de um trabalho científico 13,00
Estudo autónomo 29,00
Frequência das aulas 14,00
Trabalho laboratorial 25,00
Total: 81,00

Eligibility for exams

Delivery of an assignment during the evaluation process.

Calculation formula of final grade

Evaluation will be based on practical assignments (80%) and final exam (20%).
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