Difference between revisions of "Monte Carlo"
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* [http://modelai.gettysburg.edu/2014/mc1/index.html An Introduction to Monte Carlo Techniques in Artificial Intelligence | Todd W. Neller] | * [http://modelai.gettysburg.edu/2014/mc1/index.html An Introduction to Monte Carlo Techniques in Artificial Intelligence | Todd W. Neller] | ||
* [http://mcts.ai/about/ Monte Carlo Tree Search (MCTS)] | * [http://mcts.ai/about/ Monte Carlo Tree Search (MCTS)] | ||
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| + | A broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution. | ||
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| + | http://www.statisticalengineering.com/images/probs_figs/HowIt%20Works.gif | ||
<youtube>3gcLRU24-w0</youtube> | <youtube>3gcLRU24-w0</youtube> | ||
<youtube>kYWw6GBRjVk</youtube> | <youtube>kYWw6GBRjVk</youtube> | ||
Revision as of 10:18, 24 September 2018
- Google DeepMind AlphaGo Zero
- An Introduction to Monte Carlo Techniques in Artificial Intelligence | Todd W. Neller
- Monte Carlo Tree Search (MCTS)
A broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution.
