  Three classes of stochastic networks and their performance measures are
considered. These performance measures are defined as the expected value of
some random variables and cannot normally be obtained analytically as functions
of network parameters in a closed form. We give similar representations for the
random variables to provide a useful way of analytical study of these functions
and their gradients. The representations are used to obtain sufficient
conditions for the gradient estimates to be unbiased. The conditions are rather
general and usually met in simulation study of the stochastic networks.
Applications of the results are discussed and some practical algorithms of
calculating unbiased estimates of the gradients are also presented.
