Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
10 |
5 |
4 |
2 weeks |
60 hours |
Instructor
Cristian Marcelo Villegas Lobos
Marcelo Andrade da Silva
Objective
Introduce students to scientific programming, stochastic simulation and some computational statistical
methods. Develop fundamentals and programming logic aimed at statistical theory.
Content
Study of scientific programming with development of algorithms aimed at solving problems in Statistics.
Program content:
1 Introducing the R software
1.1 Installing R
1.2 Objects in R
1.3 Vectors, matrices and non-numeric values
2 Scientific programming techniques
2.1 Basic programming
2.2 Vector-based programming
2.3 Recursive programming
3 Computational Fundamentals in Statistics
3.1 Graphics
3.2 Probability distributions
3.3 Estimation methods
4 Stochastic Simulation
4.1 Generating random numbers
4.2 Inversion method
4.3 Rejection method
4.4 Box-Muller algorithm
Bibliography
BOWMAN, C.F. Algorithms and data structures: An Approach in C. Oxford: Oxford University Press,
2004.
CHAMBERS, J.M. Software for data analysis: Programming with R. New York: Springer, 2008.
DALGAARD, P. Introductory statistics with R. New York: Springer, 2008.
GENTLE, J.E. Random number generation and monte carlo methods. New York: Springer, 2009.
JONES, O.; MAILLARDET, R.; ROBINSON, A. Introduction to scientific programming and simulation using
R. New York: Champman & Hall, 2014.
MATLOFF, N. The art of R programming: A tour of statistical software design. San Francisco: No Starch
Press, 2011.
ROSS, S.M. Simulation. 5.ed. California: Elsevier, 2013.