R Programming for Simulation and Monte Carlo Methods

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.

Requirements

  • RStudio and R Console, popular no-cost software tools, will be required (instructions included).

Description

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
English
Size: 6.82 GB

Download Course
Source: R Programming for Simulation and Monte Carlo Methods | Udemy

The author(s) of this course invested a lot of time and effort in creating it. Please consider purchasing the course from the original author(s) if your budget permits. Your purchase motivates the author(s) to keep the course up-to-date and to provide support. The course also includes a certificate of completion. Thank you

Leave a Reply

We're On Telegram

Join our telegram channel and be the first to know when we post new courses, update courses and also when we share freebies.