PřF:MSchim Biostat. and Stat. Biocomput. - Informace o předmětu
MSchim Biostatistics and Statistical Biocomputing
Přírodovědecká fakultapodzim 2005
- Rozsah
- 2/0/0. 2 kr. (příf plus uk plus > 4). Ukončení: z.
- Vyučující
- Prof. Michael Schimek (přednášející), prof. RNDr. Ivanka Horová, CSc. (zástupce)
RNDr. Tomáš Pavlík, Ph.D. (cvičící) - Garance
- prof. RNDr. Ivanka Horová, CSc.
Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta - Rozvrh seminárních/paralelních skupin
- MSchim/01: Rozvrh nebyl do ISu vložen. T. Pavlík
- Omezení zápisu do předmětu
- Předmět je otevřen studentům libovolného oboru.
- Osnova
- Basics
The characteristics of biostatistics and statistical biocomputing with respect to applied statistics, computational biology and bioinformatics.
Design aspects (experimental, quasi-experimental, and non-experimental).
Data sources and measurement characteristics (conventional sampling, time sampling, and event sampling).
Data structures (errors, complexity, size, and dimensionality) in the modern biosciences.
The role of statistical (stochastic) methodology.
The role of computing and algorithms.
Inferential (frequentistic and Bayes) approaches and their limitations.
Statistical modelling and its assumptions (distributions, parametric relationships).
Statistical (machine) learning and its (lack of) assumptions.
Curse of dimensionality and ill-posed problems.
Complexity control, regularization and penalization.
Selected approaches and applications
Multiple testing procedures and thresholding alternatives.
Application to microarray gene expression data (n<
- Basics
The characteristics of biostatistics and statistical biocomputing with respect to applied statistics, computational biology and bioinformatics.
Design aspects (experimental, quasi-experimental, and non-experimental).
Data sources and measurement characteristics (conventional sampling, time sampling, and event sampling).
Data structures (errors, complexity, size, and dimensionality) in the modern biosciences.
The role of statistical (stochastic) methodology.
The role of computing and algorithms.
Inferential (frequentistic and Bayes) approaches and their limitations.
Statistical modelling and its assumptions (distributions, parametric relationships).
Statistical (machine) learning and its (lack of) assumptions.
Curse of dimensionality and ill-posed problems.
Complexity control, regularization and penalization.
Selected approaches and applications
Multiple testing procedures and thresholding alternatives.
Application to microarray gene expression data (n<
- Vyučovací jazyk
- Angličtina
- Další komentáře
- Předmět je vyučován jednorázově.
- Statistika zápisu (nejnovější)
- Permalink: https://is.muni.cz/predmet/sci/podzim2005/MSchim