Abstract
This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.
Translated title of the contribution | An introduction to MCMC for machine learning |
---|---|
Original language | English |
Pages (from-to) | 5-43 |
Number of pages | 39 |
Journal | Machine Learning |
Volume | 50 |
Issue number | 1-2 |
DOIs | |
Publication status | Published - Jan 2003 |
Keywords
- Markov chain Monte Carlo
- MCMC
- Sampling
- Stochastic algorithms