Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
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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.

Jensen inequality.


(Jensen inequality) If MATH and MATH for a convex function $\phi$ then MATH


For a simple r.v. MATH we have MATH hence the statement is a consequence of the definition of convexity. For general $X$ the statement follows by approximation of $X$ by a sequence of simple r.v. $X_{n}\uparrow X$ and by the proposition ( Dominated convergence theorem ).

Notation. Index. Contents.

Copyright 2007