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Basics of probability in statistical simulation and stochastic programmingPowerPoint Presentation

Basics of probability in statistical simulation and stochastic programming

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### Basics of probability in statistical simulation and stochastic programming

Lecture 2

Leonidas Sakalauskas

Institute of Mathematics and Informatics

Vilnius, Lithuania

EURO Working Group on Continuous Optimization

Content stochastic programming

- Random variables and random functions
- Law of Large numbers
- Central Limit Theorem
- Computer simulation of random numbers
- Estimation of multivariate integrals by the Monte-Carlo method

Simple remark stochastic programming

- Probability theory displays the library of mathematical probabilistic models
- Statistics gives us the manual how to choose the probabilistic model coherent with collected data
- Statistical simulation (Monte-Carlo method) gives us knowledge how to simulate random environment by computer

Random variable stochastic programming

Random variable is described by

Set of support

Probability measure

Probability measure is described by distribution function:

Probabilistic measure stochastic programming

- Probabilistic measure has three components:
- Continuous;
- Discrete (integer);
- Singular.

Continuous r.v. stochastic programming

Continuous r.v. is described by probability density function

Thus:

Continuous variable stochastic programming

If probability measure is absolutely continuous, the expected value of random function:

Discrete variable stochastic programming

Discrete r.v. is described by mass probabilities:

Discrete variable stochastic programming

If probability measure is discrete, the expected value of random function is sum or series:

Singular variable stochastic programming

Singular r.v. probabilistic measure is concentrated on the set having zero Borel measure (say, Kantor set).

Law of Large Numbers stochastic programming (Chebyshev, Kolmogorov)

hereare independent copies of r. v. ,

What did we learn ? stochastic programming

The integral

is approximated by the sampling average

if the sample size N is large, here

is the sample of copies of r.v. , distributed with the density .

Centr stochastic programmingal limit theorem (Gauss, Lindeberg, ...)

here

Beri-Es stochastic programmingsentheorem

where

What did we learn ? stochastic programming

According to the LLN:

Thus, apply CLT to evaluate the statistical error of approximation and its validity.

Example stochastic programming

Let some event occurred ntimes repeating Nindependent experiments.

Then confidence interval of probability of event :

(1,96 – 0,975 quantile of normal distribution, confidence interval – 5% )

here

If the Beri-Esseen condition is valid: !!!

Statisti stochastic programmingcal integrating …

???

Main idea – to use the gaming of a large number of random events

Statistical integration stochastic programming

Statistical simulation and Monte-Carlo method stochastic programming

(Shapiro, (1985), etc)

Simulation of random variables stochastic programming

There is a lot of techniques and methods to simulate r.v.

Let r.v. be uniformly distributed in the interval (0,1]

Then, the random variable , where

,

is distributed with the cumulative distribution function

N stochastic programming=100, 1000

Wrap-Up and conclusions stochastic programming

- the expectations of random functions, defined by the multivariate integrals, can be approximated by sampling averages according to the LLN, if the sample size is sufficiently large;
- the CLT can be applied to evaluate the reliability and statistical error of this approximation

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