Such new dem<strong>and</strong>s were formulated in the second half of the 19 th century, first <strong>and</strong>foremost owing to the development of statistical methods in the social <strong>and</strong> biologicalsciences. The complex of theories that is usually understood <strong>and</strong> taught as mathematicalstatistics took shape on these very grounds. The theory of probability enters the sphere ofissues mainly when determining whether a {given} restricted number of observations issufficient for some deductions. Exactly here we may perceive a certain (naturally relative)specificity of the stochastic problems of mathematical statistics. In the 20 th century, thesupremacy of the British (Karl Pearson, Fisher) <strong>and</strong> partly American schools in mathematicalstatistics had definitely shown itself. An increasing number of tests for the compatibility of agiven finite series of observations with some statistical hypothesis, which were offered onvarious special occasions, has led during the last years to an intensive search for generalunifying principles of mathematical statistics considered from this very point of view ofhypotheses testing (for example, in the works of Neyman <strong>and</strong> E.S. Pearson). Here, <strong>Soviet</strong>mathematicians accomplished a number of remarkable special investigations (§3), but, for us,the development <strong>and</strong> reappraisal of the general concepts of mathematical statistics, which arenow going on abroad under a great influence of the idealistic philosophy, remains a matterfor the future.In statistical physics, the issue of the sufficiency of a restricted number of observationswithdraws to the background. Here, the directly observed quantities are most often the resultsof a superposition of a great number of r<strong>and</strong>om phenomena (for example, on a molecularscale). The material for testing hypotheses on the course of separate r<strong>and</strong>om phenomena isthus collected independently of us in quite a sufficient measure. On the other h<strong>and</strong>, statisticalphysics makes especially great dem<strong>and</strong>s for developing new patterns, wider than the classicalones, of the course of r<strong>and</strong>om phenomena. The first decades of the 20 th century had beenespecially peculiar in that the physicists themselves, not being satisfied with the possibilitiesprovided by the classical theory of probability, began to create, on isolated particularoccasions, new stochastic patterns. A number of biologists <strong>and</strong> technicians encountered thesame necessity. For example, studies of the theory of diffusion had led Fokker <strong>and</strong> Planck tothe construction of the apparatus of differential equations which were later discoveredalready as general differential equations of arbitrary continuous stochastic processes withoutaftereffect (§2). Fisher introduced the same equations quite independently when studyingsome biological issues. To such investigations, that anticipate the subsequent development ofthe general concepts of probability theory, belong also the works of Einstein <strong>and</strong>Smoluchowski on the theory of the Brownian motion, a number of studies accomplished byAmerican technicians (T. Fry et al) on the issues of queuing connected in particular withproblems of exploiting telephone networks, etc. Finally, the theory of probabilityencountered most serious problems in the direction of stochastically justifying the so-calledergodic hypothesis that underpins thermodynamics. Only around 1930 mathematiciansearnestly undertook to systematize all this material. It is the most expedient to unite all thethus originated investigations under the name of general theory of stochastic processes.After ascertaining the situation in which probability theory developed during the lasttwenty years, we go on to review, under three main heads, the pertinent accomplishments of<strong>Soviet</strong> scientists.1) Extension of the classical investigation of limit theorems for sums of independent <strong>and</strong>weakly dependent r<strong>and</strong>om variables.2) General theory of stochastic processes.3) Issues in mathematical statistics.Irrespective of this methodical division, we believe that considerable work on the theory ofprobability in the <strong>Soviet</strong> Union began roughly in 1924 – 1925. S.N. Bernstein’s fundamentalresearch (see below) appeared in 1925 – 1926, <strong>and</strong> it alone would have been sufficient forconsidering that the traditions of Chebyshev, Markov <strong>and</strong> Liapunov were worthily continued.
His (Theory of <strong>Probability</strong>), which already became classical, soonfollowed. At the same time, from 1923 -1925 onward, the works of the Moscow school(Khinchin, Kolmogorov) started developing. In the beginning, they were restricted to a rathernarrow sphere of issues within the reach of the methods taken over from the theory offunctions of a real variable 3 .1.The central issue of research done by Chebyshev, Markov <strong>and</strong> Lipaunov was theascertaining of the conditions for the applicability of the normal, or Gaussian law ofdistribution to sumss n = x 1 + x 2 + … + x n (1)of a large number of independent or weakly dependent r<strong>and</strong>om terms x i . Bernstein, in afundamental memoir of 1926, completed the classical methods of studying this issue. Heessentially widened these conditions for dependent terms <strong>and</strong> was the first who rigorouslyjustified the application of the many-dimensional Gaussian law to sums of vectors in spacesof any number of dimensions. This last-mentioned result also theoretically substantiates theapplicability of the formulas of normal correlation for the case in which correlated variablesmay be considered as sums of a very large number of terms with the connection betweenthese variables being restricted by that between the corresponding, or those close to them,terms of these sums. Such, in particular, is the situation when quantitative indications, causedby an additive action of a very large number of genes, are inherited. Consequently, Bernsteinwas able to show that the Galton law of the inheritance of such indications was a corollary ofthe Mendelian laws (under the assumption that a large number of uncoupled genes actadditively) <strong>and</strong> does not contradict them at all as it had been often stated.Returning to the one-dimensional case, we may formulate the conditions for theapplicability of the normal law to sums of independent terms in the following way: Withprobability close to 1, all the terms are much less than their sum s n 4 . A natural question hereis, What limit distributions can be obtained if we only require the same for each separateterm x i (the principle of individual negligibility)? G.M. Bavli <strong>and</strong> Khinchin have recentlyanswered this question (as also did P. Lévy in France in a somewhat vague form). In the limit,we obtain the so-called infinitely divisible laws that include as particular cases the Gauss, thePoisson, <strong>and</strong> the Caushy laws. These undoubtedly deserve to be more systematicallyintroduced into statistical practice as well.In addition to this essential extension of the classical approach to the limit theoremsconcerning sums of r<strong>and</strong>om terms, we ought to indicate the following. In applications, werefer to the limit theorems when dealing with the distributions of finite sums. At the sametime, however, the existing estimates are such that in many of the most important practicalcases the guaranteed estimate of the remainder term exceeds the main term many times over 5 .Actually, quite a satisfactory estimate only exists for the simplest case of the Laplace –Bernstein theorem.Another, even more venerable subject of classical investigations in the theory ofprobability, is the issue about the conditions for the applicability of the law of large numbersto sums of independent terms. In a number of studies, Khinchin, Kolmogorov <strong>and</strong> others alsodeveloped it further. In addition to obtaining necessary <strong>and</strong> sufficient conditions, newconcepts of strong <strong>and</strong> relative stability of the sums were created <strong>and</strong> conditions for theirapplication were formulated. Finally, Khinchin discovered an absolutely new remarkableasymptotic formula for the order of the maximal deviations of consecutive sums (1) from themean, – the so-called law of the iterated logarithm, – for sequences x 1 , x 2 ,… , x n … ofindependent terms x i . Bernstein <strong>and</strong> Khinchin studied the conditions for the applicability ofthe law of large numbers to sums of dependent terms.
- Page 4 and 5: [3] I bear in mind the well-known p
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Kozuliaev, P.A. 1. Sur la répartit
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Obukhov, A.M. 1. Normal correlation
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30. Généralisations d’un théor
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22. Alcune applicazioni dei coeffic
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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