Checking date: 22/07/2022

Course: 2022/2023

Statistical Signal Processing
Study: Master in Applied and Computational Mathematics (372)

Coordinating teacher: MIGUEZ ARENAS, JOAQUIN

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
STUDENTS ARE EXPECTED TO HAVE COMPLETED The student should have basic knowledge of - probability theory and statistics - linear algebra.
Basic competences CB6 Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB7 Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. CB9 Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. General competences CG1 Ability to maintain continuous education after his/her graduation, enabling him/her to cope with new technologies. CG2 Ability to apply the knowledge of skills and research methods related to engineering. CG3 Ability to apply the knowledge of research skills and methods related to Life Sciences. CG4 Ability to contribute to the widening of the frontiers of knowledge through an original research, part of which merits publication referenced at an international level. Specific competences CE1 Ability to know the peculiarities of data acquisition and information processing in the field of biomedical signals and images. CE2 Ability to design and implement automatic learning systems for supervised and unsupervised problem solving. CE3 Ability to design estimation and decision procedures from signals and images using statistical modeling.
Description of contents: programme
DESCRIPTION OF CONTENTS: PROGRAMME · Parameter estimation - Method of moments - Maximum likelihood - Bayesian estimation · Signal Estimation - MMSE estimation - Linear estimation and prediction - Optimal filtering · Model-based signal Processing - Markov chains and processes - State space models · Hypothesis testing and classification - Wald tests - Likelihood ratio methods - Bayesian tests
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presencial hours number % Student Presence AF3 100 100 100% AF4 32 32 100% AF5 18 0 0% AF6 90 0 0% AF7 186 0 0% AF8 12 12 100% TOTAL SUBJECT 450 138 30,6%
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • C. P. Robert, G. Casella. Monte Carlo Statistical Methods. Springer. 2004
  • H. Stark, J. W. Woods. Probability and Random Processes with Applications to Signal Processing. Prentice-Hall. 2002
  • L. Wasserman. All of statistics. Springer. 2013
  • V. Poor. An introduction to signal detection and estimation. Springer. 1994

The course syllabus may change due academic events or other reasons.