The Large Deviations of Bias Point Selection

Abstract

Suppose the inputs to the system are i.i.d. random variables {X _i }, which have the scalar density function p(·). We are interested in $\rho = P\left( {\sum\nolimits_{j = 1}^n {{X_i} > na} } \right)$. We simulate with i.i.d. {S _i } whose individual density functions are q(·). In the light of Theorem 5.1.1, let us compute the variance rates for the input and output estimators, respectively.

Success is not a good criterion if there is no significant penalty for being wrong. Banging on pots always keeps the sun from being eaten by the moon during eclipses.

Anon

For non-deterministic system read ‘Inhabited by pixies’.

Anon