You will learn how to program statistical applications and Monte Carlo simulations by examining real-life cases and using R software.
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.
- Students will need to install the popular no-cost R Console and RStudio software (instructions provided).
Monte Carlo Simulation and R Programming is a R Programming for Simulation and Monte Carlo Methods course that teaches students how to program probabilistic simulations using R. Simple “real-world” examples include simulating the probability of a baseball player accumulating twenty consecutive hits in one game or estimating the total number of taxicabs when observing a certain sequence of cabs pass in succession before reaching a particular street corner after 60 minutes.
The R Programming for Simulation and Monte Carlo Methods course examines half a dozen (sometimes amusing) examples showing simulated inference estimates, including likelihoods and confidence intervals, and detailed explanations of stochastic simulation, along with how to use the R language to create own functions and perform simulated inference calculations. I provide a detailed description of how to use R to generate various characteristics of various random variable families.
In this R Programming for Simulation and Monte Carlo Methods course, you will learn how to implement various approaches to simulate continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo integration, and variance reduction. Part of the R Programming for Simulation and Monte Carlo Methods course presents the structure and programming of programs for completing mathematical and probabilistic simulations using R statistical software with 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.
- Anyone interested in learning more about programming statistical applications with R software would benefit from this R Programming for Simulation and Monte Carlo Methods course.
Created by Geoffrey Hubona, Ph.D.
Last updated 7/2020
Size: 6.82 GB