Degree Level Course

Statistical Computing

To introduce computational methods involved in statistical estimation and learning problems.

Taught by Dootika Vats

Code BSMA3014

Credits 4 Credits

Type Elective

Prerequisites None

12-Week Roadmap

Course Structure & Syllabus

For details of standard term assessment timelines and exam structures, visit our Academics page.

WEEK 1

Introduction to R, Introduction to Monte Carlo, Pseudorandom Number Generation, Sampling Discrete Random Variables: Inverse Transform Method

WEEK 2

Discrete: Accept-Reject Algorithm, Composition Method, Sampling Continuous Random Variables: Inverse Transform Method

WEEK 3

Continuous: Accept-reject Algorithm with examples, Box-Muller method

WEEK 4

Continuous: Ratio-of-Uniforms method, examples and code, miscellaneous methods in sampling, Sampling from multivariate distritbutions

Reading List

Prescribed Books & References

“Simulation” by Sheldon Ross, Elsevier, Fifth Edition
“Monte Carlo Statistical Methods” by Christian Robert and George Casella, Springer, 2004.

Faculty & Experts

About the Instructors

Dootika Vats

Assistant Professor , Department of Mathematics and Statistics , IIT Kanpur

Dootika Vats is an Assistant Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology, Kanpur. Previously, she was an NSF Postdocotoral fellow with Prof. Gareth Roberts at the University of Warwick. Her PhD was from the University of Minnesota, Twin-Cities working with Prof. Galin Jones.

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