distributionswe have wc- (wv) - -wv/ vd- - / dv vc+ - -( + ) / dv fw (wc + ( ( (cg( ( dwc- (cg( ( ) + where the last equality follows from the fact that the integrand is equal to ( ) - times the density of the gamma(aldistribution with and ( )/ the density of mn uv is given by fz (zfw ( /nm/ the proof is completed by comparing the resulting expression with the pdf of the distribution given in table corollary (relationship between normalkh and distributionslet ( and kh be independent thenz tn / prooflet zv/ because kh we have by theorem that ( nthe result follows now from the symmetry around of the pdf of and the fact that the square of tn random variable has an ( ndistribution convergence of random variables recall that random variable is function from ohm to if we have sequence of random variables (for instancexn (ox(on for each ohm)then one can consider the pointwise convergencelim xn (ox( )nfor all ohmin which case we say that converges surely to more interesting type of convergence uses the probability measure associated with sure convergence definition convergence in probability the sequence of random variables converges in probability to random variable iffor all lim [|xn xe np we denote the convergence in probability as xn - convergence in probability