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.

11 sections • 107 lectures • 11h 42m total length

13 lectures • 1hr 14minReview of Vectors, Matrices, Lists and Functions

Preview01:39
Install R and RStudio
00:45
Review: Vectors, Matrices, Lists (part 1)
08:07
Preview06:34
Sequences and Replications (part 1)
07:12
Preview05:56
Sort and Order
04:45
Creating a Matrix (part 1)
08:51
Using Matrices (part 2)
03:19
List Structures and Horsekicks (part 1)
09:43
Dpois() Function and Horsekicks (part 2)
09:56
Sampling from a Dataframe
04:24
Section 1 Exercises
02:25

8 lectures • 58minSimulation Examples: Tossing a Coin

R Expressions Exercises Answers (part 1)
07:36
R Expressions Exercises Answers (part 2)
07:08
Preview07:13
Preview07:25
Preview07:20
Preview07:17
Preview06:16
Preview07:57

7 lectures • 39minSimulation Examples: Returning Checked Hats

11 lectures • 56minSimulation Examples: Collecting Baseball Cards and "Streaky" Behavior

Solution to Checking Hats Exercise
05:45
Preview05:52
Preview05:11
Preview05:05
Preview07:03
Preview00:27
Collecting State Quarters Exercise Solution
05:56
Preview05:33
Preview06:16
Preview05:40
"Streaky" Behavior Exercise
03:27

12 lectures • 1hr 19minMonte Carlo Methods for Inference

Solution to "Streaky" Behavior Exercise
08:53
Using Monte Carlo Simulation to Estimate Inference
05:34
Sleepless in Seattle (part 1)
07:14
Sleepless in Seattle (part 2)
04:19
Applying Monte Carlo Methods to Inference (part 1)
06:04
Applying Monte Carlo Methods to Inference (part 2)
05:46
Applying Monte Carlo Methods to Inference (part 3)
08:56
Applying Monte Carlo Methods to Inference (part 4)
09:54
Applying Monte Carlo Methods to Inference (part 5)
09:09
Comparing Estimators: The Taxi Problem (part 1)
05:26
Comparing Estimators: The Taxi Problem (part 2)
06:36
Late to Class Again ? Exercise
01:14

11 lectures • 1hr 14minStochastic Simulation and Random Variable Generation

Late to Class Again Exercise Solution
11:20
What is Stochastic Simulation ?
06:51
Simulation and Random Variable Generation (part 1)
08:33
Simulation and Random Variable Generation (part 2)
08:16
Simulation and Random Variable Generation (part 3)
04:02
Preview08:12
Simulating Discrete Random Variables (part 2)
07:00
Simulating Discrete Random Variables (part 3)
03:39
Root Finding: Newton-Raphson Technique (part 1)
07:21
Root Finding: Newton-Raphson Technique (part 2)
07:35
Create Random Variables Exercise
01:01

10 lectures • 1hr 3minInverse and General Transforms

Create Random Variables Exercise Solution (part 1)
05:07
Create Random Variables Exercise Solution (part 2)
07:59
Inverse Transforms (part 1)
06:18
Inverse Transforms (part 2)
09:22
General Transformations (part 1)
05:23
General Transformations (part 2)
08:07
Preview06:52
Accept-Reject Method (part 2)
05:51
Accept-Reject Methods (part 3)
07:55
Random Variable (Poisson) Exercise 2
1 page

12 lectures • 1hr 16minSimulating Numerical Integration

Random Variable Exercise Solution (part 1)
06:27
Random Variable Exercise Solution (part 2)
06:46
Introduction to Simulating Numerical Integration (part 1)
05:15
Introduction to Simulating Numerical Integration (part 2)
05:59
Simpson's Rule for Trapezoidal Approximation
08:24
Simulating Numerical Integration (part 1)
06:07
Preview06:14
More on Simpson's Rule
06:10
Simpson's Rule with phi Functions
09:13
Phi Functions Exercises
01:22
Hit and Miss (part 1)
06:49
Hit and Miss (part 2)
07:06

6 lectures • 47minPermutation Tests

9 lectures • 1hr 13minSimulation Case Studies: Seed Dispersal

Solution to Permutation Tests Exercises
06:07
Preview07:23
Seed Dispersal Case: Creating Classes and Functions (part 1)
08:07
Seed Dispersal Case: Creating Classes and Functions (part 2)
07:54
Seed Dispersal Case (part 1)
07:11
Preview06:10
Seed Dispersal Case (part 3)
07:41
Seed Dispersal Case (part 4)
10:22
Finish Seed Dispersal Case
11:50

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.

Geoffrey Hubona, Ph.D.

Associate Professor of Information Systems

4.1 Instructor Rating
3,617 Reviews
26,222 Students
28 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 at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). 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.

This course includes:

Training 5 or more people?

R Programming for Simulation and Monte Carlo Methods