R Programming for Simulation and Monte Carlo Methods

R Programming for Simulation and Monte Carlo Methods, available at $64.99, has an average rating of 3.7, with 107 lectures, based on 448 reviews, and has 4859 subscribers.

You will learn about 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. This course is ideal for individuals who are You do NOT need to be experienced with R software and you do NOT need to be an experienced programmer. or Course is good for practicing quantitative analysis professionals. or Course is good for graduate students seeking research data and scenario analysis skills. or Anyone interested in learning more about programming statistical applications with R software would benefit from this course. It is particularly useful for You do NOT need to be experienced with R software and you do NOT need to be an experienced programmer. or Course is good for practicing quantitative analysis professionals. or Course is good for graduate students seeking research data and scenario analysis skills. or Anyone interested in learning more about programming statistical applications with R software would benefit from this course.

Enroll now: R Programming for Simulation and Monte Carlo Methods

Summary

Title: R Programming for Simulation and Monte Carlo Methods

Price: $64.99

Average Rating: 3.7

Number of Lectures: 107

Number of Published Lectures: 107

Number of Curriculum Items: 107

Number of Published Curriculum Objects: 107

Original Price: $159.99

Quality Status: approved

Status: Live

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.

Who Should Attend

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.

Target Audiences

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.

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) spuRspackage to demonstrate how to structure and write programs to accomplish mathematical and probabilistic simulations using R statistical software.

Course Curriculum

Chapter 1: Review of Vectors, Matrices, Lists and Functions

Lecture 1: Course Introduction

Lecture 2: Install R and RStudio

Lecture 3: Review: Vectors, Matrices, Lists (part 1)

Lecture 4: Review: Vectors, Matrices, Lists (part 2)

Lecture 5: Sequences and Replications (part 1)

Lecture 6: Sort and Order

Lecture 7: Using Matrices (part 2)

Lecture 8: Sequences and Replications (part 2)

Lecture 9: Creating a Matrix (part 1)

Lecture 10: List Structures and Horsekicks (part 1)

Lecture 11: Dpois() Function and Horsekicks (part 2)

Lecture 12: Sampling from a Dataframe

Lecture 13: Section 1 Exercises

Chapter 2: Simulation Examples: Tossing a Coin

Lecture 1: R Expressions Exercises Answers (part 1)

Lecture 2: R Expressions Exercises Answers (part 2)

Lecture 3: Introduction to Simulation: A Game of Tossing a Coin (part 1)

Lecture 4: Introduction to Simulation: A Game of Tossing a Coin (part 2)

Lecture 5: Write a Simulation Function (part 1)

Lecture 6: Write a Simulation Function (part 2)

Lecture 7: Continue Coin Tossing Simulation (part 3)

Lecture 8: Continue Coin Tossing Simulation (part 4)

Chapter 3: Simulation Examples: Returning Checked Hats

Lecture 1: Random Permutations: Hat Problem (part 1)

Lecture 2: Random Permutations: Hat Problem (part 2 )

Lecture 3: Random Permutations: Hat Problem (part 3)

Lecture 4: Random Permutations: Hat Problem (part 4)

Lecture 5: Random Permutations: Hat Problem (part 5)

Lecture 6: Random Permutations: Hat Problem (part 6)

Lecture 7: Checking Hats Exercise

Chapter 4: Simulation Examples: Collecting Baseball Cards and Streaky Behavior

Lecture 1: Solution to Checking Hats Exercise

Lecture 2: Collecting Baseball Cards Simulation (part 1)

Lecture 3: Collecting Baseball Cards Simulation (part 2)

Lecture 4: Collecting Baseball Cards Simulation (part 3)

Lecture 5: Collecting Baseball Cards Simulation (part 4)

Lecture 6: Collecting Quarters Exercise

Lecture 7: Collecting State Quarters Exercise Solution

Lecture 8: Streaky Baseball Batting Behavior (part 1)

Lecture 9: Streaky Baseball Batting Behavior (part 2)

Lecture 10: Streaky Baseball Batting Behavior (part 3)

Lecture 11: Streaky Behavior Exercise

Chapter 5: Monte Carlo Methods for Inference

Lecture 1: Solution to Streaky Behavior Exercise

Lecture 2: Using Monte Carlo Simulation to Estimate Inference

Lecture 3: Sleepless in Seattle (part 1)

Lecture 4: Sleepless in Seattle (part 2)

Lecture 5: Applying Monte Carlo Methods to Inference (part 1)

Lecture 6: Applying Monte Carlo Methods to Inference (part 2)

Lecture 7: Applying Monte Carlo Methods to Inference (part 3)

Lecture 8: Applying Monte Carlo Methods to Inference (part 4)

Lecture 9: Applying Monte Carlo Methods to Inference (part 5)

Lecture 10: Comparing Estimators: The Taxi Problem (part 1)

Lecture 11: Comparing Estimators: The Taxi Problem (part 2)

Lecture 12: Late to Class Again ? Exercise

Chapter 6: Stochastic Simulation and Random Variable Generation

Lecture 1: Late to Class Again Exercise Solution

Lecture 2: What is Stochastic Simulation ?

Lecture 3: Simulation and Random Variable Generation (part 1)

Lecture 4: Simulation and Random Variable Generation (part 2)

Lecture 5: Simulation and Random Variable Generation (part 3)

Lecture 6: Simulating Discrete Random Variables (part 1)

Lecture 7: Simulating Discrete Random Variables (part 2)

Lecture 8: Simulating Discrete Random Variables (part 3)

Lecture 9: Root Finding: Newton-Raphson Technique (part 1)

Lecture 10: Root Finding: Newton-Raphson Technique (part 2)

Lecture 11: Create Random Variables Exercise

Chapter 7: Inverse and General Transforms

Lecture 1: Create Random Variables Exercise Solution (part 1)

Lecture 2: Create Random Variables Exercise Solution (part 2)

Lecture 3: Inverse Transforms (part 1)

Lecture 4: Inverse Transforms (part 2)

Lecture 5: General Transformations (part 1)

Lecture 6: General Transformations (part 2)

Lecture 7: Accept-Reject Method (part 1)

Lecture 8: Accept-Reject Method (part 2)

Lecture 9: Accept-Reject Methods (part 3)

Lecture 10: Random Variable (Poisson) Exercise 2

Chapter 8: Simulating Numerical Integration

Lecture 1: Random Variable Exercise Solution (part 1)

Lecture 2: Random Variable Exercise Solution (part 2)

Lecture 3: Introduction to Simulating Numerical Integration (part 1)

Lecture 4: Introduction to Simulating Numerical Integration (part 2)

Lecture 5: Simpsons Rule for Trapezoidal Approximation

Lecture 6: Simulating Numerical Integration (part 1)

Lecture 7: Simulating Numerical Integration (part 2)

Lecture 8: More on Simpsons Rule

Lecture 9: Simpsons Rule with phi Functions

Lecture 10: Phi Functions Exercises

Lecture 11: Hit and Miss (part 1)

Lecture 12: Hit and Miss (part 2)

Chapter 9: Permutation Tests

Lecture 1: Phi Functions (Numerical Integration) Exercise Solution

Lecture 2: Permutation Tests on a Distribution: Chckwts Example (part 1)

Lecture 3: Permutation Tests on a Distribution: Chckwts Example (part 2)

Lecture 4: Permutation Tests on a Distribution: Chckwts Example (part 3)

Lecture 5: Permutation Tests on a Distribution: Chckwts Example (part 4)

Lecture 6: Finish Permutation Tests and an Exercise

Chapter 10: Simulation Case Studies: Seed Dispersal

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 14 votes

2 stars: 33 votes

3 stars: 72 votes

4 stars: 159 votes

5 stars: 170 votes

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