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Course, academic year 2023/2024
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Stochastic Modelling in Biology - NSTP069
Title: Stochastické modelování v biologii
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2014
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:2/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: cancelled
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: prof. Lev Klebanov, DrSc.
Class: DS, pravděpodobnost a matematická statistika
Classification: Mathematics > Probability and Statistics
Interchangeability : NMST562
Is incompatible with: NMTP572
Annotation -
Last update: T_KPMS (14.05.2010)
This course is designed to familiarize students with applications of stochastic processes and mathematical statistics in biology, e.g. survival models, testing, etc.
Aim of the course -
Last update: T_KPMS (14.05.2010)

The aim of the lecture to interpret some parts of statistics and stochastic processes, typically used in some problems of modern biology.

Literature - Czech
Last update: T_KPMS (14.05.2010)

Taylor, H.M. and Karlin, S. An Introduction to Stochastic Modeling. 3rd Edition, Academic Press, Inc., 1998.

L.J.S. Allen (2003) An Introduction to Stochastic Processes with Applications to Biology. Pearson Prentice

Hall.

J.K. Percus (2002) Mathematics of Genome Analysis. Cambridge University Press, New York.

Teaching methods -
Last update: T_KPMS (17.05.2011)

Lecture.

Syllabus -
Last update: T_KPMS (14.05.2010)

1. Background

1.1 Stochastic processes and statistics.

1.2 Limit theorems (approximation techniques) .

1.3 Survival theory.

1.4 Methods of multivariate analysis.

1.5 Multiple testing theory

2. Show how stochastic models arise naturally in biology.

2.1 Population growth, epidemics, gene regulatory networks, etc.

2.2 Stochastic (bio) chemical kinetic models.

2.3 Survival and cure modeling.

2.4 Statistical analysis of microarrays.

 
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