R Programming for Simulation and Monte Carlo Methods
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R Programming for Simulation and Monte Carlo Methods
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Torrent File Content (108 files)
R Programming for Simulation and Monte Carlo Methods
10 Simulation Case Studies Seed Dispersal
009 Finish Seed Dispersal Case.mp4 -
10 Simulation Case Studies Seed Dispersal
009 Finish Seed Dispersal Case.mp4 -
93.19 MB
01 Review of Vectors, Matrices, Lists and Functions
002 Install R and RStudio.mp4 -
002 Install R and RStudio.mp4 -
5.35 MB
003 Review Vectors, Matrices, Lists part 1.jpeg -
287.17 KB
003 Review Vectors, Matrices, Lists part 1.mp4 -
33.28 MB
004 Review Vectors, Matrices, Lists part 2.mp4 -
30.06 MB
005 Sequences and Replications part 1.mp4 -
35.2 MB
006 Sequences and Replications part 2.mp4 -
28.14 MB
007 Sort and Order.mp4 -
27.33 MB
008 Creating a Matrix part 1.mp4 -
49.83 MB
009 Using Matrices part 2.mp4 -
20.02 MB
010 List Structures and Horsekicks part 1.mp4 -
57.33 MB
011 Dpois Function and Horsekicks part 2.mp4 -
48.41 MB
012 Sampling from a Dataframe.mp4 -
21.46 MB
013 Section 1 Exercises.mp4 -
11.32 MB
02 Simulation Examples Tossing a Coin
001 R Expressions Exercises Answers part 1.mp4 -
001 R Expressions Exercises Answers part 1.mp4 -
25.4 MB
002 R Expressions Exercises Answers part 2.mp4 -
30.75 MB
003 Introduction to Simulation A Game of Tossing a Coin part 1.mp4 -
37.79 MB
004 Introduction to Simulation A Game of Tossing a Coin part 2.mp4 -
39.59 MB
005 Write a Simulation Function part 1.mp4 -
44.16 MB
006 Write a Simulation Function part 2.mp4 -
16.67 MB
007 Continue Coin Tossing Simulation part 3.mp4 -
32.14 MB
008 Continue Coin Tossing Simulation part 4.mp4 -
42.09 MB
03 Simulation Examples Returning Checked Hats
001 Random Permutations Hat Problem part 1.mp4 -
001 Random Permutations Hat Problem part 1.mp4 -
19.46 MB
002 Random Permutations Hat Problem part 2 .mp4 -
35.22 MB
003 Random Permutations Hat Problem part 3.mp4 -
33.35 MB
004 Random Permutations Hat Problem part 4.mp4 -
33.82 MB
005 Random Permutations Hat Problem part 5.mp4 -
26.53 MB
006 Random Permutations Hat Problem part 6.mp4 -
31.91 MB
007 Checking Hats Exercise.mp4 -
13.8 MB
04 Simulation Examples Collecting Baseball Cards and Streaky Behavior
001 Solution to Checking Hats Exercise.mp4 -
001 Solution to Checking Hats Exercise.mp4 -
27.39 MB
002 Collecting Baseball Cards Simulation part 1.mp4 -
38.44 MB
003 Collecting Baseball Cards Simulation part 2.mp4 -
32.6 MB
004 Collecting Baseball Cards Simulation part 3.mp4 -
29.9 MB
005 Collecting Baseball Cards Simulation part 4.mp4 -
46.35 MB
006 Collecting Quarters Exercise.mp4 -
2.45 MB
007 Collecting State Quarters Exercise Solution.mp4 -
38.74 MB
008 Streaky Baseball Batting Behavior part 1.mp4 -
26.48 MB
009 Streaky Baseball Batting Behavior part 2.mp4 -
34.48 MB
010 Streaky Baseball Batting Behavior part 3.mp4 -
25.43 MB
011 Streaky Behavior Exercise.mp4 -
19.1 MB
05 Monte Carlo Methods for Inference
001 Solution to Streaky Behavior Exercise.mp4 -
001 Solution to Streaky Behavior Exercise.mp4 -
52.15 MB
002 Using Monte Carlo Simulation to Estimate Inference.mp4 -
34.56 MB
003 Sleepless in Seattle part 1.mp4 -
45.43 MB
004 Sleepless in Seattle part 2.mp4 -
32.14 MB
005 Applying Monte Carlo Methods to Inference part 1.mp4 -
43.05 MB
006 Applying Monte Carlo Methods to Inference part 2.mp4 -
46.47 MB
007 Applying Monte Carlo Methods to Inference part 3.mp4 -
68.25 MB
008 Applying Monte Carlo Methods to Inference part 4.mp4 -
71.68 MB
009 Applying Monte Carlo Methods to Inference part 5.mp4 -
59.53 MB
010 Comparing Estimators The Taxi Problem part 1.mp4 -
37.82 MB
Description
Miscellaneous
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.
What are the requirements?
Students will need to install the popular no-cost R Console and RStudio software (instructions provided).
What am I going to get from this course?
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.
Full details
What is the target audience?
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.
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