bounded-error probabilistic computation

Bounded-error probabilistic computation involves algorithms that make decisions with a high probability of correctness—usually at least 99%. These algorithms use randomness to solve problems more efficiently than deterministic methods. While there's a small chance they might give the wrong answer, this error probability stays within a predefined, acceptable limit. Such approaches are common in fields like cryptography and complex problem-solving, balancing efficiency with reliability. Essentially, they leverage randomness to get results quickly, with a guaranteed maximum likelihood of error.