  We investigate the relation between frequentist and Bayesian approaches.
Namely, we find the "frequentist" Bayes prior \pi_{f}(\lambda,x_{obs}) =
-\frac{\int_{-\infty}^{x_{obs}}\frac{\partial f(x,\lambda)}{\partial
\lambda}dx}{f(x_{obs},\lambda)} (here f(x,\lambda) is the probability density)
for which the results of frequentist and Bayes approaches to the determination
of confidence intervals coincide. In many cases (but not always) the
"frequentist" prior which reproduces frequentist results coincides with the
Jeffreys prior.
