Introduction to Monte Carlo Methods

What you'll learn
Apply MCMC to Statistical Modeling
Greater understanding of statistical methods for simulation
How to write code in R or Python
How to perform nonparametric bootstrap
Apply optimization techniques to solve numerical and combinatorial problems
At the end of this course you will learn how to apply Monte Carlo methods to Bayesian problems for data analysis
Build genetic algorithms

Requirements
You should have some experience with R or Python
This course is ideally meant for students in a graduate degree program (i.e. math, statistics, electrical engineering)
If you don't have a solid background with statistics, you should at least be willing to learn
You should have a basic understanding of mathematical statistics and desire to apply Monte Carlo methods

Description
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.

Who this course is for?
Graduate students
Bayesians
Data Scientists
Professionals
Researchers

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