Introduction to Monte Carlo Methods
Introduction to Monte Carlo Methods
.MP4, AVC, 1280x720, 25 fps | English, AAC, 2 Ch | 4h 45m | 1.16 GB
Instructor: Jonathan Navarrete
.MP4, AVC, 1280x720, 25 fps | English, AAC, 2 Ch | 4h 45m | 1.16 GB
Instructor: Jonathan Navarrete
Statistical Computation, MCMC and Bayesian Statistics
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
Introduction to Monte Carlo Methods
