Introduction to Stochastic Programming

Review of the book
There is a French proverb that says “the art of management is in anticipation”. Probability and statistics are used to predict events that are random and uncertain. The goal of probabilistic programming is to give us the optimal solution of models in which there is uncertainty. Probability is the other side of certainty that can lead to linear and non-linear models. The main goal of this book is to provide a comprehensive overview of probabilistic programming without requiring too much background for the reader.

Table of Contents
The topics covered in this book are as follows:
1- Introduction and examples
2- Problems of modeling and uncertainty
3- Theory and basic features
4- Value of information and possible answers
5- Two-stage problems with recourse
6- multi-stage probabilistic models
7- possible integer programming
8- Mathematical hope estimation and how to calculate
9- Monte Carlo methods
10- multi-stage approximation
11- Sample distribution functions