STA 755   Stochastic Modeling I


 

Topics Covered:

      Review of elementary probability theory, random variables, conditional probability and conditional expectation. Discrete time discrete state space Markov chains, the Poisson process.

 

Prerequisites: Advanced calculus and linear algebra, STA667 and MATH657 or permission of instructor
Textbook: Sheldon Ross,  Introduction to Probability Models,    9th Edition
Class Time and Location:   TR 10:00-11:15am -- CBC C321
Instructor: Amei Amei
Office: CBC-B412
Telephone: 702-895-5159
E-mail:
amei.amei@unlv.edu
Office Hours: TR  12:00-2:00pm and by appointment  

 

  Homework Assignments

 

  Supplement Text:  Stochastic Modeling of Scientific Data,  by Peter Guttorp.

                                      Probability and Random Processes,  by G. R. Grimmett and D. R. Stirzaker.

                                              

 Computing:  Use of R statistical software will be required. R is a free software environment for statistical computing and graphics available at http://www.r-project.org.

 Grading:        30%   Homework
                          30%   Midterm
                          40%  Final Exam
 

 

 

 

 Please Note:  

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