Stohastilised

Stohastilised is an Estonian adjective used to describe systems, models or processes that involve randomness or probabilistic variation. In contrast to deterministic models, stohastilised approaches incorporate probability distributions and random fluctuations, reflecting uncertainty in inputs, evolution, or measurement. The term is common in mathematics, statistics and the applied sciences when uncertainty cannot be ignored.

A central concept is the stochastic process, a family of random variables indexed by time or another parameter. Other key ideas include random variables, probability distributions, and expectations. Common types are discrete-time and continuous-time processes, Markov chains, and diffusion processes such as Brownian motion. Stohastilised models often emphasize dependence structures, variance, and long-run behavior rather than single fixed outcomes.

Stohastilised modelling is widely used across many fields. In finance, it underpins asset-price modelling and risk assessment. In physics and chemistry, diffusion and random motion are naturally described stohastilised. In biology, stochastic models capture population dynamics and genetic variation. Engineering uses stohastilised concepts in reliability, queuing theory and signal processing, while computer science relies on Monte Carlo methods and random sampling to solve complex problems.

Analytical and numerical methods for stohastilised models include solving stochastic differential equations, time-series analysis, Monte Carlo simulation, and ergodic theory. Model validation commonly involves statistical estimation, hypothesis testing and backtesting against observed data, with emphasis on capturing uncertainty, variability and robustness of conclusions.