On methods for incorporating evidences into posterior scoring of hypotheses

In likelihood ratio based decision methods, often a variable number of input evidences is used. A decision based on many such inputs can result in nearly the same likelihood ratio as one based on few inputs. We consider methods for distinguishing between such situations. One of these is to provide confidence intervals together with the decisions and another is to combine the inputs using weights. We suggest a new method that generalizes Bayesian approach and uses an explicitly defined discount function. Empirical evaluation shows greater flexibility of the designed method.