Oxford Thesis Collection

Thesis

Computationally intensive methods for Hidden Markov Models with applications to statistical genetics

Abstract:: In most fields of technology and science, the exponential increase of available data is an apparent trend. In genetics, the main contributor to this trend is the improving efficiency of sequencing technologies. While the Human Genome project focused on assembling a single reference sequence not long ago, now there are aims to sequence million genomes in upcoming projects.

The consequent computational challenge is being able to utilise this wealth of data, which requires the dev...
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APA Style

Kecskemethy, P. (2014). Computationally intensive methods for Hidden Markov Models with applications to statistical genetics [PhD thesis]. University of Oxford.

MLA Style

Kecskemethy, P. Computationally Intensive Methods for Hidden Markov Models with Applications to Statistical Genetics. 2014. University of Oxford, PhD thesis.

Chicago Style

Kecskemethy, P. 2014. “Computationally Intensive Methods for Hidden Markov Models with Applications to Statistical Genetics.” PhD thesis, University of Oxford.
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Files:: DPhilThesis_PeterKecskemethy_v56AfterCorrections.pdf

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Authors

+ Kecskemethy, P More by this author

Division:: MPLS
Department:: Statistics
Role:: Author

Contributors

Role:: Supervisor

+ Merton College, Oxford More from this funder

+ Engineering & Physical Sciences Research Council More from this funder

DOI:: 10.5287/ora-yrgk2qgxr
Type of award:: DPhil
Level of award:: Doctoral
Awarding institution:: University of Oxford

UUID:: uuid:8dd5d68d-27e9-4412-868c-0477e438a2c5
Deposit date:: 2017-09-26
ARK identifier:: ark:/29072/ora_8dd5d68d27e94412868c0477e438a2c5

Terms of use

Copyright holder:: Kecskemethy, P
Copyright date:: 2014

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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