STA 756   Stochastic Modeling II


 

Topics Covered:

        Continuous time Markov chains;  statistical inference of stochastic models;  hidden Markov chains;  diffusion processes;  renewal theory;  simulation techniques;  Markov chain Monte Carlo methods.

 

Prerequisites: STAT 755
Textbook: Sheldon Ross,  Introduction to Probability Models,    9th Edition
Class Time and Location:   TR 10:00-11:15am -- CBC C321
Instructor: Amei Amei
Office: CDC-814
Telephone: 702-895-5159
E-mail:
amei.amei@unlv.edu
Office Hours: TR  12:00-1:00pm and by appointment  

 

  Homework Assignments

 

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

                                      Bayesian Computation with R,  by Jim Albert.

                                      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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