R Programming for Simulation and Monte Carlo Methods
What you'll learn
- Use R software to program probabilistic simulations, often called Monte Carlo simulations.
- Use R software to program mathematical simulations and to create novel mathematical simulation functions.
- Use existing R functions and understand how to write their own R functions to perform simulated inference estimates, including likelihoods and confidence intervals, and to model other cases of stochastic simulation.
- Be able to generate different different families (and moments) of both discrete and continuous random variables.
- Be able to simulate parameter estimation, Monte-Carlo Integration of both continuous and discrete functions, and variance reduction techniques.
Requirements
- Students will need to install the popular no-cost R Console and RStudio software (instructions provided).
Description
R Programming for Simulation and Monte Carlo Methods focuses on using R software to program probabilistic simulations, often called Monte Carlo Simulations. Typical simplified "real-world" examples include simulating the probabilities of a baseball player having a 'streak' of twenty sequential season games with 'hits-at-bat' or estimating the likely total number of taxicabs in a strange city when one observes a certain sequence of numbered cabs pass a particular street corner over a 60 minute period. In addition to detailing half a dozen (sometimes amusing) 'real-world' extended example applications, the course also explains in detail how to use existing R functions, and how to write your own R functions, to perform simulated inference estimates, including likelihoods and confidence intervals, and other cases of stochastic simulation. Techniques to use R to generate different characteristics of various families of random variables are explained in detail. The course teaches skills to implement various approaches to simulate continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo Integration, and variance reduction techniques. The course partially utilizes the Comprehensive R Archive Network (CRAN) spuRs package to demonstrate how to structure and write programs to accomplish mathematical and probabilistic simulations using R statistical software.
Who this course is for:
- You do NOT need to be experienced with R software and you do NOT need to be an experienced programmer.
- Course is good for practicing quantitative analysis professionals.
- Course is good for graduate students seeking research data and scenario analysis skills.
- Anyone interested in learning more about programming statistical applications with R software would benefit from this course.
Instructor
- 4.1 Instructor Rating
- 4,223 Reviews
- 32,349 Students
- 24 Courses
Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS and Data Analytics at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Programming using Python (undergraduate), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate), among other courses. In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.