Mathematical foundations of probability

(MASM14)

Department:
Mathematical Statistics, Centre for Mathematical Sciences, Lund University with Lund Institute of Technology

Credits:
Mon 5 (7.5 ECTS credits)

Lecturer:
Tatyana Turova, phone: +46 46 222 8543, office: 220-219, email: tatyana@maths.lth.se

Time period:
Fall 2011, period 2.

Course book:

The course will closely follow the book by A. N. Shiryaev, Probability, Springer 1996.

Prerequisites:
30 points (45 ECTS credits) in Mathematics. An introductory course in probability and a second course in probability are desirable but not necessary.

FIRST MEETING:                    October 31,   2011,

                                                       time:     10:15 -12:00

                                                     place:      MH 332 B
 

Further schedule: UPDATED!!!    

Starting November 3, lectures run 3 times a week, 2 hours each :

Tuesday 10-12 in MH:362D

Thursday 10-12 in MH:229

Friday 10-12 in MH:228, except the dates 18/11, 25/11, 19/12 when it is in MH:309C



The last lecture is on December 20.


CURRENT INFORMATION

Overview lecture and consultations: January 11, 10-12 and January 12, 10-12 in MH:227


EXAM: January 13, 13:15 - 16:00, MH:227, and January 23, 13:15 - 17:00, MH:227



LAST EXAM (UPDATED!!!): February 1, 13:15 - 15:00, MH:227


 
 

             

Read in the book "Probability" by A.N.Shiryaev (1996): pp. 131-234,

Problems: Chapter II, §1, problems 1-4, 5-8, $2 problems 1, 2, 3, $3 problems 1, 4, 7, 8, 9. §4 problems 1-8, §6 problem 1, 2, 5, 6, 7, 8, 10, 11, 13, §7 problems 1, 2.

                                  

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Content  of the course   MASM14

1. $\sigma $-algebras, measures, measurable space.

Theorem on the equivalent conditions for the finitely additive set function to be a

probability.

2. Borel $\sigma $-algebras, the measurable spaces (R , B(R) ),

$({\bf R}^n , {\cal B}({\bf R}^n) )$ and $({\bf R}^{\infty}, {\cal B}({\bf R}^{\infty}) )$.

3. Probability measures on measurable spaces. Theorem on one-to-one correspondence

between the probabilities and the distributions.

4. Measures: discrete, continuous, singular. An example of a singular measure.

    Kolmogorov's Theorem on the extension of measures in (R^{\infty} , B(R^{\infty}) )

5. Random variables. Lemma: Borel function of a random variable is a random variable.

6. Theorem on the limits of the sequences of the extended random variables.

7. $F_{\xi}$ $\sigma $-algebra. Theorem on the representation of the $F_{\xi}$-measurable

random variable.

8. Random elements. Definition of the independence. The necessary and sufficient

conditions of the independence.

9. Lebesgue integral: definition, properties.

10. Theorem on monotone convergence.

11. Fatou's Lemma.

12. Lebesgue's Theorem on Dominated convergence.

13. Chebyshev's, the Cauchy-Bunyakovskii and Jensen's inequalities.

14. Lyapunov's, Hölder's and Minkowski's inequalities.

15. Theorem on change of variables in a Lebesgue integral.

16. Fubini's theorem.

17. Conditional expectation with respect to a $\sigma $-algebra: definition and properties.

18. Theorem on taking limits under the expectation sign.

19. Characteristic functions. Uniqueness theorem.

20. Helly's Theorem.

21. Prokhorov's Theorem.

22. Continuity Theorem.