Code: | M4064 | Acronym: | M4064 |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Mathematics |
Active? | Yes |
Responsible unit: | Department of Mathematics |
Course/CS Responsible: | Master in Mathematical Engineering |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
M:CC | 4 | Study plan since 2014/2015 | 1 | - | 6 | 48 | 162 |
M:ENM | 8 | Official Study Plan since 2023/2024 | 1 | - | 6 | 48 | 162 |
M:M | 0 | Plano Oficial do ano letivo 2021 | 1 | - | 6 | 48 | 162 |
Introduction to stochastic processes.Tools for the analysis of stochastic processes and its applications in several areas, such as signal processing, information theory, finance and economics, biology and medicine. Special attention to the understanding of the concepts and methods and to its application in interdisciplinary areas using simulated and real data.
Application driven framework aiming the:
- Integration of knowledge from previous disciplines, namely Probability and Statistics and its extension towards the probabilistic and statistical analysis of Signals and Systems. introduction the mean square estimation nd optimal filtering .
- Introduction to Stochastic Modelling. Gaussian, Bernoulli and Wiener Processes. Poisson and associated random processes. Markov chains.
The student should be able to:
1. Characterize multivariate random variables (distributions, parameters and transformations). Use the characteristic function and study stochastic convergence.
2. Characterize/classify stochastic processes (s.p.): stationarity, ergodicity and estimation. Simple and joint characterization of wide sense stationary s.p. in time and frequency domain and auto and cross correlation, spectral density and coherency.
3. Stochastic modeling: independent/stationary increments, Bernoulli, Gaussian, Poisson, Wiener s.p.
4. Markov chain analysis: transient and limit behavior.
5. Application and simulation of the learnt methods using the adequate tools in problems or concrete case studies with critical interpretation of the obtained results.
Multivariate distributions. Characteristic function. Stochastic convergence.
Stochastic processes. Frequency and time domain description. Characterization, second order descriptions. Stationarity. Spectral density, cross spectral density and coherence. Ergodicity and estimation. Linear transformations. ARMA processes. Optimal linear systems.
Stochastic modeling. Case i.i.d. Study of relevant processes as Poisson, Gaussian and Wiener. Applications and simulation.
Lectures TP to present and illustrate the topics. Problems / Projects with strong laboratorial computation component using Matlab (R).
designation | Weight (%) |
---|---|
Teste | 60,00 |
Trabalho prático ou de projeto | 40,00 |
Total: | 100,00 |
designation | Time (hours) |
---|---|
Estudo autónomo | 84,00 |
Frequência das aulas | 48,00 |
Elaboração de projeto | 30,00 |
Total: | 162,00 |
Computational work / project presented according to the due schedule (P>=30%).
Continous evaluation (2 tests), with no final exam.
Final Classification: (T*12+P*8)/20.
The final classification is based on the mean of the 2 written tests (T) and the evaluation of the computational work/project (P), including the oral component (presentation and discussion) and by a written report, presented according the schedule.
At ER the final exam (E) replaces the 2 tests in the formula.
Minimum mark in each component P and T or E is 30%.
Eventual complementar evaluation for a final mark over 18 .
Any component not concluded in the schedule and/or established conditions is considered as not performed.
Test 1: Date to be fixed at the beginning of the classes (on class)
Test 2: on the date of the exam in EN
Oral presentations of component P: last classes (on class or the schedulled substitution)
Submission witten report of component P : Date to be fixed at the beginning of the classes and scheduled in Moodle.
The exams required under special statutes will consist of a written test that may be preceded by an eliminatory oral test, to assess whether the student is in the minimum conditions to try to pass the subject in the written test.
The final classification is given by the "Formula for calculating the final classification (*)".
The P component carried out in the current academic year, or the previous academic year, may be considered.
It is not possible to improve the classification of only one of the tests, nor the component (P).
Grade improvement will be made in the appeal examination.