Comprobability

Comprobability is an interdisciplinary concept that refers to the study of probability within computation. It encompasses the design and analysis of algorithms that use randomness, the formal modeling of probabilistic processes in computing systems, and methods for reasoning about uncertainty in computation and data.

The field includes probabilistic algorithms, probabilistic complexity classes, and models such as probabilistic Turing machines and randomized programs. It also covers probabilistic inference and stochastic modeling as they apply to algorithmic problems, including average-case analysis and reliability under uncertainty.

Techniques commonly used are Monte Carlo and Las Vegas algorithms, concentration bounds, sampling methods, and probabilistic data structures such as Bloom filters and Count-Min Sketch. Probabilistic programming languages and Bayesian methods provide formal tools for expressing and solving uncertain computation tasks.

Applications span data processing, search and information retrieval, databases, networking, machine learning, and cryptography. The field also emphasizes the theoretical foundations of randomness in computation, including the study of probabilistic complexity classes, randomness extraction, and the limitations of probabilistic methods.