Checking date: 11/03/2024

Course: 2024/2025

Reasoning under Uncertainty
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: CABRAS , STEFANO

Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Basic knowledge of descriptive statistics, elements of probability and inference.
The main objective is to use the concepts related to Bayesian inference for their subsequent application to problems related to IA, by means of appropriate techniques of approximation of a posteriori distribution of Bayesian models. These concepts will be illustrated within the scope of some inference models related to regression problems.
Skills and learning outcomes
Description of contents: programme
1. Bayesian inference (D. Hoff Chap 1 to 2): 1.1. Probability concepts associated with Bayesian statistics 1.2 Fundamentals. 2. Computational problems associated with Bayes' formula (D. Hoff Ch. 3 to 6): 2.1 Conjugate and non-conjugate priors. 2.2 Numerical methods: 2.2.1. Laplace approximation of the a posteriori distribution. 2.2.2. MCMC.
Learning activities and methodology
Training Activities: AF1: Synchronous theoretical teaching presentations accompanied by electronic material, such as digital presentations. AF2: E-learning activities AF3: Theoretical-practical synchronous teaching classes AF4: Laboratory practicals AF5: Tutorials AF6: Group work AF7: Individual student work AF8: Partial and final exams
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Peter D. Hoff. A First Course in Bayesian Statistical Methods. Springer. 2009
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.

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