Question: Who was active in probability theory, Lev Schnirelmann or Yuri Linnik?

Context articles: Conditional probability
Lev Schnirelmann
Convergence of random variables
Partition function (mathematics)
Inscribed square problem
Yuri Linnik
Probability theory
Probability and statistics
Concentration inequality
Radha Laha

In probability theory, conditional probability is a measure of the probability of an event given that (by assumption, presumption, assertion or evidence) another event has occurred.
 If the event of interest is "A" and the event "B" is known or assumed to have occurred, "the conditional probability of "A" given "B"", or "the probability of "A" under the condition "B"", is usually written as "P"("A"|"B"), or sometimes "P" ("A").
 For example, the probability that any given person has a cough on any given day may be only 5%.
 But if we know or assume that the person has a cold, then they are much more likely to be coughing.
 The conditional probability of coughing given that you have a cold might be a much higher 75%.
Lev Genrikhovich Schnirelmann (also Shnirelman, Shnirel'man; "Лев Ге́нрихович Шнирельма́н" ; January 2, 1905 – September 24, 1938) was a Soviet mathematician who worked on number theory, topology and differential geometry.
In probability theory, there exist several different notions of convergence of random variables.
 The convergence of sequences of random variables to some limit random variable is an important concept in probability theory, and its applications to statistics and stochastic processes.
 The same concepts are known in more general mathematics as stochastic convergence and they formalize the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behaviour that is essentially unchanging when items far enough into the sequence are studied.
 The different possible notions of convergence relate to how such a behaviour can be characterised: two readily understood behaviours are that the sequence eventually takes a constant value, and that values in the sequence continue to change but can be described by an unchanging probability distribution.
The partition function or configuration integral, as used in probability theory, information theory and dynamical systems, is a generalization of the definition of a partition function in statistical mechanics.
 It is a special case of a normalizing constant in probability theory, for the Boltzmann distribution.
 The partition function occurs in many problems of probability theory because, in situations where there is a natural symmetry, its associated probability measure, the Gibbs measure, has the Markov property.
 This means that the partition function occurs not only in physical systems with translation symmetry, but also in such varied settings as neural networks (the Hopfield network), and applications such as genomics, corpus linguistics and artificial intelligence, which employ Markov networks, and Markov logic networks.
 The Gibbs measure is also the unique measure that has the property of maximizing the entropy for a fixed expectation value of the energy; this underlies the appearance of the partition function in maximum entropy methods and the algorithms derived therefrom.
The inscribed square problem, also known as the square peg problem or the Toeplitz' conjecture, is an unsolved question in geometry: "Does every plane simple closed curve contain all four vertices of some square?"
 This is true if the curve is convex or piecewise smooth and in other special cases.
 The problem was proposed by Otto Toeplitz in 1911.
 Some early positive results were obtained by Arnold Emch and Lev Schnirelmann.
 s of 2016 the general case remains open.
Yuri Vladimirovich Linnik (Russian: Ю́рий Влади́мирович Ли́нник ; January 8, 1915 – June 30, 1972) was a Soviet mathematician active in number theory, probability theory and mathematical statistics.
Probability theory is the branch of mathematics concerned with probability.
 Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms.
 Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space.
 Any specified subset of these outcomes is called an event.
Probability and Statistics or also called Statistics and Probability are two related but separate academic disciplines. Statistical analysis often uses probability distributions, and the two topics are often studied together.
 However, probability theory contains much that is mostly of mathematical interest and not directly relevant to statistics.
 Moreover, many topics in statistics are independent of probability theory.
In probability theory, concentration inequalities provide bounds on how a random variable deviates from some value (typically, its expected value).
 The laws of large numbers of classical probability theory state that sums of independent random variables are, under very mild conditions, close to their expectation with a large probability.
 Such sums are the most basic examples of random variables concentrated around their mean.
 Recent results show that such behavior is shared by other functions of independent random variables.
Radha Govind Laha (1 October 1930 – 14 July 1999) was a probabilist, statistician, and mathematician, known for his work in probability theory, characteristic functions, and characterisation of distributions.
 He was born in Calcutta, India and he was a student of C. R. Rao at Calcutta University, where in 1957 he earned a doctorate in analytical probability theory from the prestigious Indian Statistical Institute.
 His dissertation was entitled "Some Characterization Problems in Probability Theory and Mathematical Statistics".