Probabilistic Graphical Models
Keywords |
Classification |
Keyword |
OFICIAL |
Medicine |
Instance: 2016/2017 - SP ![Requerida a integração com o Moodle Ícone do Moodle](/fmup/pt/imagens/MoodleIcon)
Cycles of Study/Courses
Teaching language
Suitable for English-speaking students
Objectives
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.
- Explore 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
Presencial
Program
- Introduction:
1.1 Motivation and examples;
1.2 Probability and medical applciations;
1.3 Probabilistic graphical models;
1.4 Introduction to Bayesian networks.
- Bayesian networks:
2.1 Semantics and factorization;
2.2 Probabilistic influence flow;
2.3 Conditional independence andnaive Bayes;
2.4 Causal independence.
- Bayesian networks in clinical research and decision support
3.1 Diagnosis
3.2 Prognosis
3.3 Etiology and risk
- Building Bayesian networks from data
4.1 Machine learning
4.2 Bayesian network parameter estimation
4.3 Bayesian network structure learning
4.4 Learning and inferring from incomplete data.
Mandatory literature
Darwiche, A.; Modeling and Reasoning with Bayesian Networks, Cambridge University Press, 2009
Cowell, R.G., Dawid, P., Lauritzen, S.L., Spiegelhalter, D.J.; Probabilistic Networks and Expert Systems, Springer Verlag, 1999
ucas, P. J. F., van der Gaag, L. C., & Abu-Hanna, A.; Bayesian networks in biomedicine and health-care , Artificial intelligence in medicine, 2004
Darwiche, A.; Bayesian networks, Communications of the ACM, 2010 (53(12), 80–90)
Sierra, B., & Larranaga, P.; Medical Bayes Networks In International Symposium on Medical Data Analysis, Springer Verlag, 2000 (pp. 4–14)
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 will be based on group assignments and final exam.
Software
R
SamIam
keywords
Physical sciences > Mathematics > Statistics
Physical sciences > Mathematics > Probability theory
Physical sciences > Mathematics > Statistics > Medical statistics
Evaluation Type
Distributed evaluation with final exam
Assessment Components
Designation |
Weight (%) |
Exame |
40,00 |
Trabalho escrito |
60,00 |
Total: |
100,00 |
Calculation formula of final grade
Evaluation will be based on group (60%) assignments and final exam (40%).