1. Math Camp 2: Probability Theory Sasha Rakhlin

2. Introduction  -algebra Measure Lebesgue measure Probability measure Expectation and variance Convergence Convergence in probability and almost surely Law of Large Numbers. Central Limit Theorem Useful Probability Inequalities Jensen ’ s inequality Markov ’ s inequality Chebyshev ’ s inequality Cauchy-Schwarz inequality Hoeffeding ’ s inequality

3.  -algebra Let  be a set. Then a  -algebra  is a nonempty collection of subsets of  such that the following hold:    If E  ,  - E   If F i    i,  i F i  

4. Measure A measure  is a function defined on a  -algebra  over a set  with values in [0,  ] s.t.  (  ) = 0  (E) =  i  (E i ) if E =  i E i ( , ,  ) is called a measure space

5. Lebesgue measure The Lebesgue measure is the unique complete translation-invariant measure on a  -algebra s.t. ([0,1]) = 1

6. Probability measure Probability measure is a positive measure  over ( ,  ) s.t.  (  ) = 1 ( , ,  ) is called a probability space A random variable is a measurable function X:  R

7. Expectation and variance If X is a random variable over a probability space ( , ,  ), the expectation of X is defined as The variance of X is

8. Convergence x n  x if   > 0  N s.t. |x n – x| N ( X n converges to X in probability) if   > 0

9. Convergence in probability and almost surely Any event with probability 1 is said to happen almost surely. A sequence of real random variables X n converges almost surely to a random variable X iff Convergence almost surely implies convergence in probability

10. Law of Large Numbers. Central Limit Theorem Weak LLN: if X 1, X 2, … is an infinite sequence of i.i.d. random variables with  = E(X 1 ) = E(X 2 ) = …,, that is, CLT: where  is the cdf of N(0,1)

11. Jensen ’ s inequality If  is a convex function, then

12. Markov ’ s inequality If X  0 and t  0,

13. Chebyshev ’ s inequality If X is random variable and t > 0, e.g.

14. Cauchy-Schwarz inequality If E(X 2 ) and E(Y 2 ) are finite,

15. Hoeffding ’ s inequality Let a i  X i  b i for i = 1, …, n. Let S n =  X i, then for any t > 0,