period 1 <strong>and</strong> remained, until the appearance of De Moivre’s work, the only limit theorem ofthe theory of probability.4. In the next, the second period according to my reckoning, separate fields had alreadyappeared where quantitative probability-theoretic calculations were required. These fieldswere not yet numerous. The main spheres of application were the theory of errors <strong>and</strong>problems in the theory of artillery firing. The chief results obtained in the former theory wereconnected with Gauss, <strong>and</strong> the achievements in the latter subject, with Poisson 2 . Neitherfield was, however, alien for Laplace who was the main figure of that time. Here are the mainpertinent theoretical results.1) The De Moivre – Laplace limit theorem. It asymptotically estimates the probabilityP n (t) = P(µ ≤ np + t np( 1−p)that, in n independent trials, each having probability p of a positive outcome, the number ofsuch outcomes µ will not exceed np + t np( 1− p). The theorem states that, as n , P n (t)tends toP(t) = (1/t2 π ) −∞exp(– x 2 /2)dx.From then onwards, the probability distribution P(t), appearing here for the first time, isplaying a large part in the entire further theory of probability <strong>and</strong> is {now} called normal.2) The Poisson generalization of this theorem to the case of variable probabilities p 1 , p 2 , …,p n .3) The substantiation of the method of arithmetic mean {of least squares} by Gauss.4) The development of the method of characteristic functions by Laplace.Thus, not only from the ideological <strong>and</strong> philosophical side, but in the regular everydayscientific work, the main attention was transferred from the elementary theorems about afinite number of events to limit theorems. Accordingly, non-elementary analytic methodswere dominating.Note that the maturity of the contemporary Russian science revealed itself in thatLobachevsky’s probability-theoretic work, in spite of his remote peripheral scientific interestin the theory of probability, was quite on a level with international science <strong>and</strong> approvinglyquoted by Gauss 3 . Ostrogradsky also left several works in probability, but the dominantinfluence of Russian science on the entire development of probability theory begins later.5. The third period in the development of the theory of probability, i.e., the second half ofthe 19 th century, is especially interesting for us. In those times, a rapid development ofmathematical statistics <strong>and</strong> statistical physics occurred in Western Europe. However, it tookplace on a rather primitive <strong>and</strong> dated theoretical basis with Petersburg becoming the center ofstudies in the main general problems of probability. The activity of academicianBuniakovsky, who, in 1846, published an excellent for his time treatise, ! (Principles of the Mathematical Theory of<strong>Probability</strong>), <strong>and</strong> widely cultivated applications of probability to insurance, statistics, <strong>and</strong>,especially, demography, <strong>and</strong> paved the way for the flourishing of the Petersburg school ofprobability theory.It was Pafnuty Lvovich Chebyshev, however, who brought the Russian theory ofprobability to the first place in the world. From a methodological aspect, the principalupheaval accomplished by him consisted not only in that he was the first to dem<strong>and</strong>, with
categorical insistence, absolute rigor in proving limit theorems 4 . The main point is that ineach instance Chebyshev strove to determine exact estimates of the deviations from limitregularities taking place even in large but finite numbers of trials in the form of inequalitiesunconditionally true for any number of these.Furthermore, Chebyshev was the first to clearly appreciate <strong>and</strong> use the full power of theconcepts of r<strong>and</strong>om variable <strong>and</strong> its expectation (mean value) 5 . These notions were knownearlier <strong>and</strong> are derivatives of the main concepts, event <strong>and</strong> probability. However, they aresubordinated to a much more convenient <strong>and</strong> flexible algorithm. This is true to such an extentthat we now invariably replace the examination of event A by considering its characteristicr<strong>and</strong>om variable A equal to unity when A occurs, <strong>and</strong> to zero otherwise. The probability P(A)of event A is then nothing but the expectation E A of A . Only much later the appropriatemethod of characteristic functions of sets came to be systematically used in the theory offunctions of a real variable.The celebrated {Bienaymé –} Chebyshev inequalityP(|| ≥ k) ≤ E/kis also quite in the spirit of the later theory of functions. Nowadays such a method ofestimating appears to us quite natural <strong>and</strong> goes without saying. In Chebyshev’s time,however, when the similar way of thinking was alien to analysis or the theory of functions(the concept of measure did not exist!), this simple method was absolutely new.Having given his main attention to the concept of r<strong>and</strong>om variable, Chebyshev wasnaturally led to consider limit theorems on the number of positive outcomes in a series oftrials as subordinated to more general propositions on the sums of r<strong>and</strong>om variables 6 . Thecelebrated Chebyshev theorem appeared as a natural generalization of the Bernoulliproposition: If r<strong>and</strong>om variables x 1 , x 2 , …, x n , … are independent one from another, <strong>and</strong>bounded by the same constant, |x n | ≤ N, then, for any > 0, a limit relationP(|(s n /n) – E(s n /n)| > ) 0 as n exists for the arithmetic means (s n /n) = (x 1 + x 2 + …+ x n )/n. Markov extremely widened theconditions of this limit relation.The ascertaining of a proposition similar to the Laplace theorem for sums of r<strong>and</strong>omvariables proved much more difficult. At the same time, this problem could not have failed toattract attention. Without solving it, the special role of the normal distribution in the theory oferrors, in artillery <strong>and</strong> other technical <strong>and</strong> natural-scientific fields could not have beenconsidered sufficiently cleared up. The problem was to ascertain under sufficiently wideconditions the limit relationtP[s n ≤ Es n + t var( s n) ] (1/ 2 π ) −∞exp(– x 2 /2)dx as n where var (s n ) = E[s n – Es n )] 2 is the so-called variance of the sum s n , equal, as it is wellknown, to the sum of the variances of the terms x i :var (s n ) = var (x 1 ) + var (x 2 ) + … + var (x n ).Here, it was impossible to manage without some sufficiently complicated analytical tool.Chebyshev chose the method of moments, i.e., the study of the quantitative characteristics ofa r<strong>and</strong>om variable x of the type m k = E(x k ). He was unable to carry through the proof of thelimit theorem by means of this method. The final success fell to Markov’s lot, but the choice
- Page 4 and 5: [3] I bear in mind the well-known p
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10. A.N. Kolmogorov. The Theory of
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Kuznetsov, Stratonovich & Tikhonov
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In the homogeneous case H s t = H t
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to such a generalization. He only s
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In the particular case of a charact
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as it is usual for the modern theor
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1. {The second reference to Pugache
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Smirnov, Romanovsky and others made
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determined the precise asymptotic c
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for finite values of N, M and R 2 .
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Mikhalevich’s findings by far exc
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Uch. Zap. = Uchenye ZapiskiUkr = Uk
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Khinchin, A.Ya. 43. Math. Ann. 101,
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7. DAN 115, 1957, 49 - 52.Pinsker,
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Anderson, T.W., Darling, D.A. (1952
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Statistical problems in radio engin
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observations for its power with reg
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securing against mistakes (A.N. Kry
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of the others, then its distributio
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In Kiev, in the 1930s, N.M. Krylov