Explore MCMC methods with this repository, featuring lecture scripts, workshop slides, and code implementations for efficient sampling.
📝 Lecture scripts explaining fundamental MCMC concepts.
🖥️ Workshop slides designed for both data science and mathematical seminar audiences.
💻 Code implementations for various MCMC sampling algorithms.
📊 Workshop for Data Science: Practical insights into MCMC applications in data analysis.
🧮 Math Seminar: Covers theoretical foundations and advanced aspects of MCMC methods.
The goal of this repository is to generate a sample from a probability distribution. This becomes particularly challenging when dealing with high-dimensional data and an unknown form of the target distribution.
MCMC algorithms generate random numbers and find applications in:
Bayesian Statistics
Computing complex integrals (e.g., Expectation Maximization)
Reinforcement Learning
Physics and Econometrics
Relevance: MCMC is widely used but rarely taught.
High-Performance: Learn state-of-the-art (SOTA) algorithms applicable to your next project.
Mathematical Background in Stochastics: Familiarity with probability theory, random processes, and statistical concepts will enhance your experience with the material.
Coding Prerequisites: Basic Python knowledge.
Uncover the potential of MCMC for simulating probability distributions and making statistical inferences. Whether you're a data scientist, mathematician, or enthusiast, this repository provides valuable resources to enhance your understanding of MCMC.