Introduction to Stochastic Processes
- Details
- Category: Discipline
- Ross, S.M. (2014). Introduction to Probability Models. (11th ed.). Academic Press,New York. Ross, S.M. (1996). Stochastic Processes. (2nd ed.). John Wiley & Sons.
- Data Science
- 6321
- 23086
- Introduction to Stochastic Processes
- ISMAT6321-23086
- 2
- 6
- 0
- 12
- Não
- Português
- The teaching methodology includes the expository method (TM1) to present the necessary contents, the demonstrative (TM2) to illustrate its application to practical examples and the active one (TM3) for solving exercises.
- Mandatory
- LO1: Understand and characterize simple stochastic systems; LO2: Solve basic problems associated with the Poisson process and its variants, renewal processes, Markov chains in discrete and continuous time and Brownian motion.
- S1: Stochastic processes and their characterization S2: Poisson processes and their variants S3: Renewal processes and their variants S4: Markov chains in discrete time S5: Markov chains in continuous time S6: Brownian motion
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. A avaliação contínua inclui a realização de dois testes escritos com uma ponderação de 40% cada e exercícios práticos ao longo do semestre (20%).
- Semestral
- The curricular unit Introduction to Stochastic Processes belongs to the scientific area of Statistics and introduces the fundamental concepts of stochastic processes and their modelling. It develops skills to model random phenomena evolving over time, providing an important foundation for applications in Data Science, optimization and machine learning.
