Definition
Stochastic elements introduce variability into AI systems, such as noise in data or random initialization of weights. Unlike deterministic models, stochastic models account for uncertainty, making them suitable for complex, real-world scenarios where outcomes are not fixed but follow probability distributions.
Summary
Describes processes or models that involve randomness and probability rather than deterministic outcomes.
Key Concepts
- Randomness
- Probability
- Uncertainty
Use Cases
- Monte Carlo methods
- Generative Adversarial Networks
- Bayesian inference