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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
V. Implementation tools.
VI. Basic Math II.
1. Real Variable.
A. Operations on sets and logical statements.
B. Fundamental inequalities.
a. Cauchy and Young inequalities.
b. Cauchy inequality for scalar product.
c. Holder inequality.
d. Lp interpolation.
e. Chebyshev inequality.
f. Lyapunov inequality.
g. Jensen inequality.
h. Estimate of mean by probability series.
i. Gronwall inequality.
C. Function spaces.
D. Measure theory.
E. Various types of convergence.
F. Signed measures. Absolutely continuous and singular measures. Radon-Nikodym theorem.
G. Lebesgue differentiation theorem.
H. Fubini theorem.
I. Arzela-Ascoli compactness theorem.
J. Partial ordering and maximal principle.
K. Taylor decomposition.
2. Laws of large numbers.
3. Characteristic function.
4. Central limit theorem (CLT) II.
5. Random walk.
6. Conditional probability II.
7. Martingales and stopping times.
8. Markov process.
9. Levy process.
10. Weak derivative. Fundamental solution. Calculus of distributions.
11. Functional Analysis.
12. Fourier analysis.
13. Sobolev spaces.
14. Elliptic PDE.
15. Parabolic PDE.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Estimate of mean by probability series.


roposition

(Estimate of mean by probability series) MATH

Proof

We have MATH We estimate MATH We evaluate the MATH as follows: MATH We make the change of the summation index in the second sum $m=n+1$ : MATH The residue term estimates MATH when the series converge. Therefore, MATH





Notation. Index. Contents.


















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