In this course, you will learn to use real-life cases to examine statistical applications and Monte Carlo simulations and to program them using R.
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 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.
- RStudio and R Console, popular no-cost software tools, will be required (instructions included).
A Monte Carlo Simulation and R Programming course teaches students how to conduct probabilistic simulations Using R. A few examples are simulating a baseball player accumulating twenty consecutive hits or estimating the number of taxicabs at a particular intersection after 60 minutes based on observing a sequence of cabs pass in succession.
The R Programming for Simulation and Monte Carlo Methods course explores half a dozen (sometimes humorous) examples showing how simulated inference estimates can be calculated, including likelihoods and confidence intervals, and detailed explanations of stochastic simulation, as well as how to use the R language to create own functions and perform simulation calculations. The following section explains how to use R to produce characteristics of various random variable families.
You will learn how to simulate both continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo integration, and variance reduction in this R Programming for Simulation and Monte Carlo Methods course. As part of the R Programming for Simulation and Monte Carlo Methods course, students will construct and program programs to conduct mathematical and probabilistic simulations using R statistical software and the spuRs package from the Comprehensive R Archive Network (CRAN).
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.
- The course is good for practicing quantitative analysis professionals.
- The R Programming for Simulation and Monte Carlo Methods course is good for graduate students seeking research data and scenario analysis skills.
- This R Programming for Simulation and Monte Carlo Methods course would be of interest to anyone interested in learning more about programming statistical applications with R software.
Created by Geoffrey Hubona, Ph.D.
Last updated 7/2020
Size: 6.82 GB