Stochastic Models
Stochastic models are mathematical models that incorporate random variables to represent the uncertainty and variability present in real-world systems. These models are used in data science and artificial intelligence to simulate and analyze complex systems that involve randomness and uncertainty. Stochastic models can be used to make predictions, estimate probabilities, and optimize decision-making processes. They are widely used in fields such as finance, engineering, and operations research. Stochastic models can be classified into two main categories: discrete-time models and continuous-time models. Discrete-time models are used to model systems that evolve over time in discrete steps, while continuous-time models are used to model systems that evolve continuously over time. Examples of stochastic models include Markov chains, queuing models, and Monte Carlo simulations.
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