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- W7128883 abstract "Changes over time in a random variable make up a sequence of temporal values known as a stochastic process. Neuronal activity, depression status, and viral load are just a few examples of stochastic processes of interest in the field of biomedical research. Understanding the evolution and predicting the future status of the process under certain conditions both provide key information that is extremely useful in animal experimentation and in clinical and epidemiological studies in humans. A Markov process is a particular kind of stochastic process in which its future status will depend only on its present status, not on its past history. Since the Russian mathematician Andrei Andreyevich Markov (1856–1922) first defined this kind of process in 1906, there have been numerous applications in biomedical research. This chapter shows some of the Markov models that are currently the most widely used in the fields of biomedicine and the health sciences. The content has been drafted from an eminently practical standpoint, with particular emphasis on the main techniques for analysis, interpretation of results, the usefulness of the models, and the software available for applications. Interesting references have been included in the bibliography cited at the end of the chapter for any readers keen to learn more about the more theoretical aspects of this topic." @default.
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- W7128883 date "2008-03-06" @default.
- W7128883 modified "2023-09-25" @default.
- W7128883 title "Markov Processes for Biomedical Data Analysis" @default.
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- W7128883 doi "https://doi.org/10.1007/978-1-59745-285-4_75" @default.
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