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R Programming for Simulation and Monte Carlo Methods (Udemy.com)

Learn to program statistical applications and Monte Carlo simulations with numerous "real-life" cases and R software.

3.8
( 60 Reviews )

Created by: Geoffrey Hubona, Ph.D.

Produced in 2021

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What you will 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.

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Overall Score : 76 / 100

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Course 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.
Read More product length iconTime: 11.5h

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Instructor Details

Geoffrey Hubona, Ph.D.

Dr. Geoffrey Hubona hasheld full-time tenure-track, andtenured, assistant andassociate professorfacultypositions at 4 major state universities in the United States since 1993. Currently, he is anassociate professor of MIS at Texas A&MInternational 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 facultypositions, he taughtdozens ofvarious 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, alsofrom USF; an MBA in Financefrom 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 educationalorganizations that teachresearch methods and quantitative analysis techniques. These research methods techniquesincludelinear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.

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Reviews

3.8

60 total reviews

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By Roman Kouznetsov on 12/20/2020

4 lectures in, seems more like an intro to cs class than it does data types, but seems promising!

By Franco D'Auro on 9/25/2020

El curso es bueno. Sin embargo requiere de experiencia en programacin en lenguaje R y estadstica inferencial

By Francisco Contreras on 9/24/2020

Un excelente curso para aprender a programar simulacin, si ya es bien sabido que ya existen mtodos de programacin en este curso se van de forma mas manual y de la mano para que puedas entender el pensamiento en simulacin

By Samet Eren Alemdar on 8/15/2020

Adam ald paray hak ediyor. Keke Trk eitimciler de byle olsa.

By Alberto Martellini on 7/15/2020

It is good and well thought. However I cannot see the instructions to use the additional Resources, what format should I use to open these files?

By Bimal Sajeewa Amaradasa on 7/9/2020

This course is great match for my needs. I am reviewing this at the end of section 5. I did take this lecturer's Linear Regression course earlier and found the lectures were not organized. In that there were a lot of repeats and files downloaded were named in a confusing manner. Contrary to that I find this course more organized, so far!

By Moushumi Upadhyay on 5/23/2020

just started , Intresting

By Tim Clark on 4/11/2020

Given the price, you cannot complain too much, but you really need exercises that you can work through line by line. If you are a competent R programmer then it is OK, but for someone who is still at the beginning it is a bit too advanced.

By William Stewart on 4/6/2020

This course is helping me finish acquiring probability knowledge I have been reading about. So far so good.

By Eric Ruiz on 3/27/2020

Si muy buena

By Ralf Herold on 3/23/2020

Speaking style is not well developed, as if not reheared but improvised while going along, too colloquial. Lecturing style best be more developed to speak in clear sentences. Pronounciation could also be better. At times volume levels go into clipping, producing distortion. Also would recommend to use higher screen resolution when recording.

By Leonard Green on 3/11/2020

havent completed class yet, but already i know . i made a great decision in purchasing. That why i have all of his classes ! Now if i can find time to compete them all.

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R Programming for Simulation and Monte Carlo Methods
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  • Video Duration: icon 11.5h
  • Price: 11.99 Track Price 11.99 Track Price
  • Provider: icon Udemy
  • Year: icon 2021
  • Language: icon English
  • Certificate: icon Yes
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