Abstract
Markov modeling, otherwise called stochastic processes, assumes that per time unit the same % of a population will have an event, and it is used for long term predictions from short term observations. This chapter is to assess whether the method can be applied by non-mathematicians using an online matrix-calculator.
This chapter was previously published in “Machine learning in medicine-cookbook 1” as Chap.18, 2013.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Electronic Supplementary Material(s)
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cleophas, T.J., Zwinderman, A.H. (2020). Stochastic Processes for Long Term Predictions from Short Term Observations. In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-33970-8_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33969-2
Online ISBN: 978-3-030-33970-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)