EN
CONTENTS
Introduction.......................................................................................................................................................................... 5
I. Infinitely divisible probability measures on $R^d$....................................................................................... 6
II. The classical limit theorems for sums of independent random vectors................................................ 14
III. Convergence in law to ℒ ($\overrightarrow a$, A, µ) for sums of dependent random vectors.......... 21
IV. Convergence in law to l ($\overrightarrow a$, A, ν) for sums of dependent random vectors............ 84
V. Convergence in law to K($\overrightarrow m$, A, ϰ) for sums of dependent random vectors
with finite variances............................................................................................................................................... 47
VI. Particular cases of limit distributions........................................................................................................... 40
VII. Another method of conditioning.................................................................................................................... 57
References........................................................................................................................................................................... 58