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Encyclopedia > Andrei Markov

Andrey Andreyevich Markov (Андрей Андреевич Марков) (June 14, 1856 N.S. _ July 20, 1922) was a Russian mathematician.


Markov was born in Ryazan. He studied at St. Petersburg University in 1874 under the tutelage of Chebyshev. In 1886, he became a member of the St. Petersburg Academy of Science. He is best known for his work on theory of stochastic processes. His research later became known as Markov chains.


See also

References

  • A. A. Markov. "Rasprostranenie zakona bol'shih chisel na velichiny, zavisyaschie drug ot druga". Izvestiya Fiziko-matematicheskogo obschestva pri Kazanskom universitete, 2-ya seriya, tom 15, pp 135-156, 1906.
  • A.A. Markov. "Extension of the limit theorems of probability theory to a sum of variables connected in a chain". reprinted in Appendix B of: R. Howard. Dynamic Probabilistic Systems, volume 1: Markov Chains. John Wiley and Sons, 1971.

External links

  • Andrei Andreyevich Markov (http://www-gap.dcs.st-and.ac.uk/~history/Mathematicians/Markov.html) (at the MacTutor History of Mathematics archive)
  • The Life and Work of AA Markov (http://mac03-204ha.math.ncsu.edu/~langville/naoumov.pdf)
  • Andrey Andreevich Markov (1903-1979) (http://logic.pdmi.ras.ru/Markov/) (page at the Steklov Institute of Mathematics at St.Petersburg)







  Results from FactBites:
 
Andrey Markov Summary (1675 words)
Markov involved himself in anti-Czarist, liberal politics and protested Czar Nicholas II (1868-1918) refusal to elect writer Maxim Gorky (1868-1936) to the St. Petersburg Academy in 1902.
Markov was born in Ryazan, Russia, on June 14, 1856.
Markov received his bachelor's degree in 1878 for a thesis on differential equations and continuing fractions, for which he was also awarded a gold medal.
Markov's inequality (177 words)
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.
Markov's inequality (and other similar inequalities) relate probabilities to expectations, and provide (frequently) loose but still useful bounds for the distribution function of a random variable.
Markov's inequality is actually just one of a wider class of inequalities relating probabilities and expectations, that are all examples of a single theorem.
  More results at FactBites »


 
 

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