Stochastic Models

Stochastic models are mathematical frameworks that incorporate randomness or uncertainty into their processes, using probability distributions to represent variables that evolve over time in non-deterministic ways. They are widely applied in fields like finance, operations research, and machine learning to analyze systems subject to random fluctuations, such as stock prices, queueing networks, or biological processes. These models help predict outcomes, assess risks, and optimize decisions under uncertainty by simulating possible scenarios based on probabilistic rules.

Also known as: Probabilistic Models, Random Process Models, Stochastic Processes, Uncertainty Models, Stochastics

Why learn Stochastic Models?

Developers should learn stochastic models when working on projects involving risk analysis, predictive modeling, or simulations where randomness is a key factor, such as in algorithmic trading, supply chain optimization, or reinforcement learning algorithms. They are essential for building robust systems that account for variability, enabling more accurate forecasts and better decision-making in uncertain environments like financial markets or dynamic resource allocation.

Learning Resources

• tutorial: Introduction to Stochastic Processes
• tutorial: Stochastic Modeling in Python
• course: Coursera: Stochastic Processes
• docs: Wikipedia: Stochastic Model
• book: Book: Introduction to Probability Models by Sheldon Ross