Checking date: 11/04/2019


Course: 2019/2020

Graphical and Hidden Markov Models
(17769)
Study: Master in Statistics for Data Science (345)
EPI


Coordinating teacher: WIPER , MICHAEL PETER

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Competences and skills that will be acquired and learning results.
Graphical representation of conditional independence; Learning when to use and how to fit both discrete and Gaussian graphical models; fitting and interpreting log linear models; hidden Markov models; estimation and interpretation of hidden Markov models; use of statistical software for model fitting.
Description of contents: programme
1) Basic concepts of graphical models a) Directed and non-directed graphs. b) Conditional independence and its graphical representation c) Representing graphical models in R d) The naive Bayes classifier as a graphical model 2) Log-linear models a) Representation as graphical models b) Fitting log-linear models c) Practical example 3) Bayesian networks a) Representation b) Classical and Bayesian approaches c) How to infer causality d) Practical example 4) Gaussian networks and mixed networks a) Representation and adjustment b) Practical example 5) More complex graphical models a) Algorithms b) Examples 6) Hidden Markov models a) Structure. b) Algorithms for estimation. c) Interpretation of hidden states. d) Practical examples. e) Quick fitting of hidden Markov models: filters.
Learning activities and methodology
Theoretical and computer practical classes with presentation and resolution of real problems, individual and group work.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • D. Bellot. Learning Probabilistic Graphical Models in R. PACKT Publishing. 2016
  • J. Chapmann. Markov Models: Introduction to Markov Chains, Hidden Markov Models and Bayesian Networks. CreateSpace Independent Publishing Platform. 2017
  • L. Sucar. Probabilistic Graphical Models: Principles and Applications. Springer. 2015
  • S. Hojsgaard, D. Edwards, S. Lauritzen. Graphical Models with R. Springer. 2012
Recursos electrónicosElectronic Resources *
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The course syllabus and the academic weekly planning may change due academic events or other reasons.