Definition
Monte Carlo techniques are a class of computational algorithms that rely on repeated random sampling to estimate mathematical quantities. They are particularly useful in high-dimensional integration, optimization, and probabilistic inference where closed-form solutions are unavailable. By generating thousands or millions of random scenarios, these methods approximate the expected value or distribution of outcomes. In AI, they are essential for Bayesian inference, reinforcement learning exploration strategies, and evaluating complex risk models in uncertain environments.
Summary
Monte refers to Monte Carlo methods, which use repeated random sampling to obtain numerical results for problems that are deterministic in principle but too complex for analytical solutions.
Key Concepts
- Random Sampling
- Numerical Approximation
- Probabilistic Inference
- Law of Large Numbers
Use Cases
- Bayesian posterior estimation
- Robotics path planning under uncertainty
- Option pricing in finance