Introduction to Monte Carlo Methods
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# Introduction to Monte Carlo Methods

Statistical Computation, MCMC and Bayesian Statistics
New
3.9 (4 ratings)
21 students enrolled
Last updated 9/2017
English
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Includes:
• 3.5 hours on-demand video
• 1 Article
• 25 Supplemental Resources
• Access on mobile and TV
• Certificate of Completion

### Training 5 or more people?

What Will I Learn?
• Apply MCMC to Statistical Modeling
• Greater understanding of statistical methods for simulation
• How to write code in R
• How to perform nonparametric bootstrap
• Apply MCMC to Machine Learning
• At the end of this course you will learn how to apply Monte Carlo methods to Bayesian problems for data analysis
View Curriculum
Requirements
• You should have some experience with R or a general purpose programming language
• You should have a basic understanding of mathematical statistics
• If you don't have a solid background with statistics, you should at least be willing to learn
Description

This course will teach your the fundamentals of Monte Carlo methods for you to apply to your research, work or resume.  You'll learn advanced techniques for Monte Carlo methods for generating random variables, integration and inference.

Learn how to create confidence intervals, monitor convergence and nonparametric bootstrap methods. Dive into the EM and MC-EM algorithms for handling missing data.

Explore the principles of Markov Chain Monte Carlo and learn how to apply MCMC to Bayesian analysis in logistic regression and change-point time series analysis

Code these all these algorithms "by hand"

All students need to obtain the lecture notes/slides for self-study

Who is the target audience?
• Anyone who wants to understand Monte Carlo methods
• Bayesians
• Data Scientists
• Professionals
• Researchers
Compare to Other Math & Science Courses
Curriculum For This Course
25 Lectures
03:20:40
+
Introduction
4 Lectures 26:41

Lecture slides

Preview 02:30

Resource Materials

Resource Materials
1 question

Review lecture

Review
07:57

Preview 15:58

Introduction to Monte Carlo Simulation
00:16
+
Generating Random Variables
4 Lectures 31:43

Generating Uniform Random Samples

Uniform Random Variables
06:28

Binomial Example

Transformation Methods
03:19

Inverse Transform Method

Inverse Transform Method
07:35

Accept Reject Algorithm

Preview 14:21
+
Monte Carlo Integration
4 Lectures 28:10
Simple Monte Carlo Integration
06:45

Calculating Tail Probabilities
04:59

Importance Sampling
08:09

Importance Sampling Examples
08:17
+
Variance Estimation and Acceleration
5 Lectures 30:33
Intro to Bootstrap
07:33

Paired Bootstrap Example
05:53

Monitoring Convergence
09:26

Antithetic Variables
06:35

Exercise
01:06
+
Expectation Maximization
2 Lectures 19:39
EM Algorithm
14:43

Monte Carlo EM
04:56
+
MCMC and Metropolis Hastings
4 Lectures 44:45
Preview 04:52

Markov Chains part 2
09:10

Metropolis Hastings algorithms
20:23

Bayesian Logistic Regression
10:20
+
Gibbs Samplers
2 Lectures 19:09
Gibbs Sampler algorithm
08:16

Bayesian Change Point Analysis
10:53