Bayesian statistics approach to chess engines optimization

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 should apply all situations where want certain gain/loss function which has no analytical form and thus cannot be measured directly only by comparison two parameter sets. also experimentally compare with famous SPSA method.