  We develop a new method for stochastic optimization using the Bayesian
statistics approach. More precisely, we optimize parameters of chess engines as
those data are available to us, but the method should apply to all situations
where we want to optimize a certain gain/loss function which has no analytical
form and thus cannot be measured directly but only by comparison of two
parameter sets. We also experimentally compare the new method with the famous
SPSA method.
