Question: Who was born first, Andrey Markov or Askold Khovanskii?

Context articles: Markov algorithm
Markov–Krein theorem
Markov's inequality
Vladimir Markov (mathematician)
Stochastic matrix
Gauss–Markov process
Markov brothers' inequality
Askold Khovanskii
Andrey Markov
Markov chain

In theoretical computer science, a Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols.
 Markov algorithms have been shown to be Turing-complete, which means that they are suitable as a general model of computation and can represent any mathematical expression from its simple notation.
 Markov algorithms are named after the Soviet mathematician Andrey Markov, Jr.
In probability theory, the Markov–Krein theorem gives the best upper and lower bounds on the expected values of certain functions of a random variable where only the first moments of the random variable are known.
 The result is named after Andrey Markov and Mark Krein.
In probability theory, Markov's inequality gives an upper bound for the probability that a non-negative function of a random variable is greater than or equal to some positive constant.
 It is named after the Russian mathematician Andrey Markov, although it appeared earlier in the work of Pafnuty Chebyshev (Markov's teacher), and many sources, especially in analysis, refer to it as Chebyshev's inequality (sometimes, calling it the first Chebyshev inequality, while referring to Chebyshev's inequality as the second Chebyshev's inequality) or Bienaymé's inequality.
Vladimir Andreevich Markov (Russian: Влади́мир Андре́евич Ма́рков ; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov.
 He died of tuberculosis at the age of 25.
In mathematics, a stochastic matrix (also termed probability matrix, transition matrix, substitution matrix, or Markov matrix) is a square matrix used to describe the transitions of a Markov chain.
 Each of its entries is a nonnegative real number representing a probability.
 It has found use throughout a wide variety of scientific fields, including probability theory, statistics, mathematical finance and linear algebra, as well as computer science and population genetics.
 The stochastic matrix was first developed by Andrey Markov at the beginning of the 20th century.
Gauss–Markov stochastic processes (named after Carl Friedrich Gauss and Andrey Markov) are stochastic processes that satisfy the requirements for both Gaussian processes and Markov processes.
 The stationary Gauss–Markov process (also known as a Ornstein–Uhlenbeck process) is a very special case because it is unique, except for some trivial exceptions.
In mathematics, the Markov brothers' inequality is an inequality proved in the 1890s by brothers Andrey Markov and Vladimir Markov, two Russian mathematicians.
 This inequality bounds the maximum of the derivatives of a polynomial on an interval in terms of the maximum of the polynomial.
 For "k" = 1 it was proved by Andrey Markov, and for "k" = 2,3... by his brother Vladimir Markov.
Askold Georgievich Khovanskii (Russian: Аскольд Георгиевич Хованский ; born 3 June 1947, Moscow) is a Russian and Canadian mathematician currently a professor of mathematics at the University of Toronto, Canada.
 His areas of research are algebraic geometry, commutative algebra, singularity theory, differential geometry and differential equations. His research is in the development of the theory of toric varieties and Newton polyhedra in algebraic geometry.
 He is also the inventor of the theory of fewnomials.
Andrey (Andrei) Andreyevich Markov (Russian: Андре́й Андре́евич Ма́рков , in older works also spelled Markoff) (14 June 1856 N.S. – 20 July 1922) was a Russian mathematician.
 He is best known for his work on stochastic processes.
 A primary subject of his research later became known as Markov chains and Markov processes.
In probability theory and related fields, a Markov process, named after the Russian mathematician Andrey Markov, is a stochastic process that satisfies the Markov property (sometimes characterized as "memorylessness").
 Roughly speaking, a process satisfies the Markov property if one can make predictions for the future of the process based solely on its present state just as well as one could knowing the process's full history, hence independently from such history; i.e., conditional on the present state of the system, its future and past states are independent.