Lecture Notes
MEASURE THEORY AND STOCHASTIC PROCESSES Lecture Notes José Melo, Susana Cruz Faculdade de Engenharia da Universidade do Porto Programa Doutoral em Engenharia Electrotécnica e de Computadores March 2011 Contents 1 Probability Space3 1 Sample space Ω .....................................4 2 σ-eld F .........................................4 3 Probability Measure P .................................6 3.1 Measure µ ....................................6 3.2 Probability Measure P .............................7 4 Learning Objectives..................................7 5 Appendix........................................8 2 Chapter 1 Probability Space Let's consider the experience of throwing a dart on a circular target with radius r (assuming the dart always hits the target), divided in 4 dierent areas as illustrated in Figure 1.1. 4 3 2 1 Figure 1.1: Circular Target The circles that bound the regions 1, 2, 3, and 4, have radius of, respectively, r , r , 3r , and . 4 2 4 r Therefore, the probability that a dart lands in each region is: 1 , 3 , 5 , P (1) = 16 P (2) = 16 P (3) = 16 7 . P (4) = 16 For this kind of problems, the theory of discrete probability spaces suces. However, when it comes to problems involving either an innitely repeated operation or an innitely ne op- eration, this mathematical framework does not apply. This motivates the introduction of a measure-theoretic probability approach to correctly describe those cases. We dene the proba- bility space (Ω; F;P ), where Ω is the sample space, F is the event space, and P is the probability 3 measure. Each of them will be described in the following subsections. 1 Sample space Ω The sample space Ω is the set of all the possible results or outcomes ! of an experiment or observation.
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