Stochastic Processes

Stochastic processes are mathematical models used to describe the evolution of a system over time, where the evolution is subject to random fluctuations. In data science and artificial intelligence, stochastic processes are used to model and analyze complex systems that exhibit random behavior. Stochastic processes can be used to model a wide range of phenomena, including stock prices, weather patterns, and the spread of diseases. They are also used in machine learning algorithms, such as Markov Chain Monte Carlo (MCMC) methods, to simulate and analyze complex systems. Stochastic processes are characterized by their probability distributions, which describe the likelihood of different outcomes at different points in time. The most common types of stochastic processes include random walks, Poisson processes, and Brownian motion.
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