Multistage stochastic optimization problems appear in many ways in
finance, insurance, energy production and trading, logistics and
transportation, among other areas. They describe decision situations
under uncertainty and with a longer planning horizon. This book contains
a comprehensive treatment of today's state of the art in multistage
stochastic optimization. It covers the mathematical backgrounds of
approximation theory as well as numerous practical algorithms and
examples for the generation and handling of scenario trees. A special
emphasis is put on estimation and bounding of the modeling error using
novel distance concepts, on time consistency and the role of model
ambiguity in the decision process. An extensive treatment of examples
from electricity production, asset liability management and inventory
control concludes the book.