Introduction to Monte Carlo Methods

Introduction to Monte Carlo Methods

Hi all,

As of June, 2022, I have to let you know that this course is out of date. I am busy with life and will not be able to respond to any messages or questions. The content was written when I was applying to grad schools  in 2018 so it's a little rough, but the material will help anyone in their MS statistics/cs/economics programs. I unfortunately cannot change the pricing to free due to Udemy's policies, but I hope this provides people with some basic understanding of Monte Carlo methods and statistical computing.

Best wishes,

Jonathan

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This is a fully developed graduate-level course on Monte Carlo methods open to the public. I simplify much of the work created leaders in the field like Christian Robert and George Casella into easy to digest lectures with examples. 

The target audience is anyone with a background in programming and statistics with a specific interest in Bayesian computation. 

In this course, students tackle problems of generating random samples from target distributions through transformation methods and Markov Chains, optimizing numerical and combinatorial problems (i.e. Traveling Salesman Problem) and Bayesian computation for data analysis.

In this course, students have the opportunity to develop Monte Carlo algorithms into code "by hand" without needing to use "black-box" 3rd party packages.

Statistical Computation, MCMC and Bayesian Statistics

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What you will learn

• Apply MCMC to Statistical Modeling
• Greater understanding of statistical methods for simulation
• How to write code in R or Python

Rating: 3.95

Level: Intermediate Level

Duration: 5 hours

Instructor: Jonathan Navarrete