The value of
information in stochastic programming with partial information on probability
distribution** **

**Abstract:** In many stochastic programming problems the value of the
probability distribution may be partially known for the decision maker (DM) for many
reasons (e.g. the information is too expansive). Stochastic program with linear partial
information on probability distribution (SPI) is one of the models which deals with
problems where we have an imprecise probability distribution. If the DM has the
opportunity to get an additional information about the value of probability distribution,
he may ask for the value of such information. In this paper we are interested in the value
of perfect information and partial information on probability distribution in SPI
problems. Some Bounds on such value are given based on the notion of upper and lower
probability and the minimax theory.

**Keywords**: Stochastic programming, Partial linear information, Upper and
lower probability, Minimax problems.