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Stochastic Processes

1. Measure and Integration

Measurable Spaces

Monotone class theorem

Let \(\mathcal C\) be a class of subset closed under finite intersections and containing \(\Omega\) (that is, \(\mathcal C\) is a \(\pi\)-system). Let \(\mathcal B\) be the smallest class containing \(\mathcal C\) which is closed under increasing limits and by difference (that is, \(\mathcal B\) is the smallest \(\lambda\) system containing \(\mathcal C\)). Then \(\mathcal B = \sigma(\mathcal C)\)

Dynkin \(\pi-\lambda\) theorem

If \(P\) is a \(\pi\) system and \(D\) is a \(\lambda\) system with \(P\subseteq D\), then \(\sigma(P) \subseteq D\).

Measurable Functions

Measures

Integration

Transforms and Indefinite Integrals

Kernels and Product Spaces

2. Probability Spaces

Probability Spaces and Random

Expectations

Lp-spaces and Uniform Integrability

Information and Determinability

Independence

3. Convergence

4. Conditioning

5. Martingales and Stochastics

6. Poisson Random Measures

7. L´evy Processes

8. Brownian Motion

9. Markov Processes

References

1. Cinlar E, Cınlar E. Probability and stochastics. Springer; 2011.