Title:
Markov processes
Authors:
Havill, Jessen T.
Citation:
Markov processes. Computational Science Across the Curriculum, Capital University.
Publisher:
Computational Science Across the Curriculum
DATE ISSUED:
2008
PERMANENT LINK:
http://hdl.handle.net/2374.DEN/5003; http://hdl.handle.net/2374
Type:
Article
Language:
en_US
Description:
We present an elementary introduction to Markov processes, applied to two relatively simple biological problems. We first look at Mendel's original experiments as a Markov chain, and then tackle the problem of finding CpG islands in DNA sequences with a hidden Markov model (HMM). The presentation, unlike most textbooks, assumes only a basic familiarity with discrete probability, matrix multiplication, and limits. We also show how Python (and the Numpy module) can be used to apply these mathematical concepts to real-sized problems. A basic familiarity with Python programming is assumed for these portions. Occasional "Questions" and "Reflections" encourage students to pause to engage with the content. Answers to "Questions" can be found in an Appendix, while "Reflections" are answered in the text.
Appears in Collections:
Faculty Publications

Full metadata record

DC FieldValue Language
dc.contributor.authorHavill, Jessen T.en
dc.date.accessioned2013-01-02T16:26:24Zen
dc.date.accessioned2013-12-18T21:04:56Z-
dc.date.available2013-01-02T16:26:24Zen
dc.date.available2013-12-18T21:04:56Z-
dc.date.created2008en
dc.date.issued2008en
dc.identifier.citationMarkov processes. Computational Science Across the Curriculum, Capital University.en_US
dc.identifier.urihttp://hdl.handle.net/2374.DEN/5003en
dc.identifier.urihttp://hdl.handle.net/2374-
dc.descriptionWe present an elementary introduction to Markov processes, applied to two relatively simple biological problems. We first look at Mendel's original experiments as a Markov chain, and then tackle the problem of finding CpG islands in DNA sequences with a hidden Markov model (HMM). The presentation, unlike most textbooks, assumes only a basic familiarity with discrete probability, matrix multiplication, and limits. We also show how Python (and the Numpy module) can be used to apply these mathematical concepts to real-sized problems. A basic familiarity with Python programming is assumed for these portions. Occasional "Questions" and "Reflections" encourage students to pause to engage with the content. Answers to "Questions" can be found in an Appendix, while "Reflections" are answered in the text.en_US
dc.language.isoen_USen_US
dc.publisherComputational Science Across the Curriculumen_US
dc.relation.ispartofFaculty Publicationsen_US
dc.titleMarkov processesen_US
dc.typeArticleen_US
dc.contributor.institutionDenison Universityen_US
dc.date.digitized2013-01-02en
dc.contributor.repositoryDenison Resource Commonsen_US
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