Random Variables and Simulations
9
Monte Carlo Methods
Statistical Computing
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Introduction
R Programming
1
Basic R Programming
2
Control Flow
3
Functional Programming
4
Scripting and Piping in R
5
Further Resources
Random Variables and Simulations
6
Random Variables
7
Models
8
Random Number Generator
9
Monte Carlo Methods
10
Markov Chain Monte Carlo Methods
Randomizations
11
Permutation Tests
12
Permutation Regression
Monte Carlo Methods
13
Monte Carlo Integration
14
Monte Carlo Hypothesis Testing
15
Monte Carlo Optimization
16
Monte Carlo Methods Case Study 1
17
Monte Carlo Methods Case Study 2
18
Monte Carlo Methods Case Study 3
Bootstrapping
19
Parametric Bootrapping
20
Nonparametric Boostrapping
Data Manipulation, Summarization, and Graphics
21
Importing Data
22
Data Manipulation
23
Data Summarization
24
Graphics
Reporting Data
25
Quarto Documents
26
Presentations
Table of contents
9.1
Probability Inverse Transformation
9.2
Composition Method
9.3
Acceptance-Rejection Method
9.4
Box-Muller Methods
Random Variables and Simulations
9
Monte Carlo Methods
9
Monte Carlo Methods
Monte Carlo Methods are used to determine the
9.1
Probability Inverse Transformation
9.2
Composition Method
9.3
Acceptance-Rejection Method
9.4
Box-Muller Methods
8
Random Number Generator
10
Markov Chain Monte Carlo Methods