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Cancer Modeling: How Far Can We Go?

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Book cover Cancer Mortality and Morbidity Patterns in the U.S. Population

Part of the book series: Statistics for Biology and Health ((SBH))

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Notes

  1. 1.

    A Markov process, named after the Russian mathematician Andrey Markov (1856–1922), is a mathematical model for the random evolution characterized by having a “lack-of-memory” property, i.e., the conditional distribution of what happens in the future given everything up to now and depends only on the present state, so, the future and past are independent. This work founded a completely new branch of probability theory and launched the theory of stochastic processes. Markov also made some studies of poetry and poetic styles, applying the ideas of his theory to analysis of vowels and consonants in literary texts.

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    K.G. Manton, Igor Akushevich & Julia Kravchenko

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Manton, K., Akushevich, I., Kravchenko, J. (2009). Cancer Modeling: How Far Can We Go?. In: Cancer Mortality and Morbidity Patterns in the U.S. Population. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78193-8_2

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