[Ethireclya]: (Udemy) R Programming for Simulation and Monte Carlo Methods

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R Programming for Simulation and Monte Carlo Methods 10 Simulation Case Studies Seed Dispersal
  • 009 Finish Seed Dispersal Case.mp4 (93.2 MB)
  • 001 Solution to Permutation Tests Exercises.mp4 (37.4 MB)
  • 002 Seed Dispersal Case Study Object Orientation.mp4 (46.3 MB)
  • 003 Seed Dispersal Case Creating Classes and Functions part 1.mp4 (53.0 MB)
  • 004 Seed Dispersal Case Creating Classes and Functions part 2.mp4 (52.5 MB)
  • 005 Seed Dispersal Case part 1.mp4 (39.3 MB)
  • 006 Seed Dispersal Case part 2.mp4 (37.5 MB)
  • 007 Seed Dispersal Case part 3.mp4 (56.5 MB)
  • 008 Seed Dispersal Case part 4.mp4 (73.6 MB)
01 Review of Vectors, Matrices, Lists and Functions
  • 002 Install R and RStudio.mp4 (5.4 MB)
  • 003 Review Vectors, Matrices, Lists part 1.jpeg (287.2 KB)
  • 003 Review Vectors, Matrices, Lists part 1.mp4 (33.3 MB)
  • 004 Review Vectors, Matrices, Lists part 2.mp4 (30.1 MB)
  • 005 Sequences and Replications part 1.mp4 (35.2 MB)
  • 006 Sequences and Replications part 2.mp4 (28.1 MB)
  • 007 Sort and Order.mp4 (27.3 MB)
  • 008 Creating a Matrix part 1.mp4 (49.8 MB)
  • 009 Using Matrices part 2.mp4 (20.0 MB)
  • 010 List Structures and Horsekicks part 1.mp4 (57.3 MB)
  • 011 Dpois Function and Horsekicks part 2.mp4 (48.4 MB)
  • 012 Sampling from a Dataframe.mp4 (21.5 MB)
  • 013 Section 1 Exercises.mp4 (11.3 MB)
  • 001 Course Introduction.mp4 (6.6 MB)
02 Simulation Examples Tossing a Coin
  • 001 R Expressions Exercises Answers part 1.mp4 (25.4 MB)
  • 002 R Expressions Exercises Answers part 2.mp4 (30.7 MB)
  • 003 Introduction to Simulation A Game of Tossing a Coin part 1.mp4 (37.8 MB)
  • 004 Introduction to Simulation A Game of Tossing a Coin part 2.mp4 (39.6 MB)
  • 005 Write a Simulation Function part 1.mp4 (44.2 MB)
  • 006 Write a Simulation Function part 2.mp4 (16.7 MB)
  • 007 Continue Coin Tossing Simulation part 3.mp4 (32.1 MB)
  • 008 Continue Coin Tossing Simulation part 4.mp4 (42.1 MB)
03 Simulation Examples Returning Checked Hats
  • 001 Random Permutations Hat Problem part 1.mp4 (19.5 MB)
  • 002 Random Permutations Hat Problem part 2 .mp4 (35.2 MB)
  • 003 Random Permutations Hat Problem part 3.mp4 (33.4 MB)
  • 004 Random Permutations Hat Problem part 4.mp4 (33.8 MB)
  • 005 Random Permutations Hat Problem part 5.mp4 (26.5 MB)
  • 006 Random Permutations Hat Problem part 6.mp4 (31.9 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 (27.4 MB)
  • 002 Collecting Baseball Cards Simulation part 1.mp4 (38.4 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 (46.4 MB)
  • 006 Collecting Quarters Exercise.mp4 (2.4 MB)
  • 007 Collecting State Quarters Exercise Solution.mp4 (38.7 MB)
  • 008 Streaky Baseball Batting Behavior part 1.mp4 (26.5 MB)
  • 009 Streaky Baseball Batting Behavior part 2.mp4 (34.5 MB)
  • 010 Streaky Baseball Batting Behavior part 3.mp4 (25.4 MB)
  • 011 Streaky Behavior Exercise.mp4 (19.1 MB)
05 Monte Carlo Methods for Inference
  • 001 Solution to Streaky Behavior Exercise.mp4 (52.1 MB)
  • 002 Using Monte Carlo Simulation to Estimate Inference.mp4 (34.6 MB)
  • 003 Sleepless in Seattle part 1.mp4 (45.4 MB)
  • 004 Sleepless in Seattle part 2.mp4 (32.1 MB)
  • 005 Applying Monte Carlo Methods to Inference part 1.mp4 (43.1 MB)
  • 006 Applying Monte Carlo Methods to Inference part 2.mp4 (46.5 MB)
  • 007 Applying Monte Carlo Methods to Inference part 3.mp4 (68.2 MB)
  • 008 Applying Monte Carlo Methods to Inference part 4.mp4 (71.7 MB)
  • 009 Applying Monte Carlo Methods to Inference part 5.mp4 (59.5 MB)
  • 010 Comparing Estimators The Taxi Problem part 1.mp4 (37.8 MB)
  • 011 Comparing Estimators The Taxi Problem part 2.mp4 (43.0 MB)
  • 012 Late to Class Again Exercise.mp4 (6.5 MB)
06 Stochastic Simulation and Random Variable Generation
  • 001 Late to Class Again Exercise Solution.mp4 (61.5 MB)
  • 002 What is Stochastic Simulation .mp4 (31.0 MB)
  • 003 Simulation and Random Variable Generation part 1.mp4 (47.1 MB)
  • 004 Simulation and Random Variable Generation part 2.mp4 (58.9 MB)
  • 005 Simulation and Random Variable Generation part 3.mp4 (26.5 MB)
  • 006 Simulating Discrete Random Variables part 1.mp4 (49.6 MB)
  • 007 Simulating Discrete Random Variables part 2.mp4 (41.3 MB)
  • 008 Simulating Discrete Random Variables part 3.mp4 (18.2 MB)
  • 009 Root Finding Newton-Raphson Technique part 1.mp4 (27.7 MB)
  • 010 Root Finding Newton-Raphson Technique part 2.mp4 (45.6 MB)
  • 011 Create Random Variables Exercise.mp4 (5.5 MB)
07 Inverse and General Transforms
  • 001 Create Random Variables Exercise Solution part 1.mp4 (22.8 MB)
  • 002 Create Random Variables Exercise Solution part 2.mp4 (35.3 MB)
  • 003 Inverse Transforms part 1.mp4 (26.9 MB)
  • 004 Inverse Transforms part 2.mp4 (50.1 MB)
  • 005 General Transformations part 1.mp4 (29.9 MB)
  • 006 General Transformations part 2.mp4 (47.7 MB)
  • 007 Accept-Reject Method part 1.mp4 (32.8 MB)
  • 008 Accept-Reject Method part 2.mp4 (30.6 MB)
  • 009 Accept-Reject Methods part 3.mp4 (37.9 MB)
08 Simulating Numerical Integration
  • 001 Random Variable Exercise Solution part 1.mp4 (42.2 MB)
  • 002 Random Variable Exercise Solution part 2.mp4 (13.6 MB)
  • 003 Introduction to Simulating Numerical Integration part 1.mp4 (25.8 MB)
  • 004 Introduction to Simulating Numerical Integration part 2.mp4 (33.3 MB)
  • 005 Simpsons Rule for Trapezoidal Approximation.mp4 (44.3 MB)
  • 006 Simulating Numerical Integration part 1.mp4 (31.8 MB)
  • 007 Simulating Numerical Integration part 2.mp4 (42.3 MB)
  • 008 More on Simpsons Rule.mp4 (38.2 MB)
  • 009 Simpsons Rule with phi Functions.mp4 (56.6 MB)
  • 010 Phi Functions Exercises.mp4 (5.5 MB)
  • 011 Hit and

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.

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[Ethireclya]: (Udemy) R Programming for Simulation and Monte Carlo Methods


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[Ethireclya]: (Udemy) R Programming for Simulation and Monte Carlo Methods


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