Stochastic Simulation Suite

A Python toolkit for simulating and visualizing fundamental stochastic processes.

Core Features

• Galton Board Simulation: Visualizes how random processes can lead to a Normal distribution (Central Limit Theorem). Outputs a histogram image.

• Pseudo Random Walks: Simulates particle diffusion on a line, showing the distribution approaching Normal over time. Outputs path and distribution plots.

• Markov Chain Steady-State Analysis: Calculates long-term equilibrium probabilities for systems (like city migration) using various numerical methods (Power Iteration, Eigenvalue, Linear System, Monte Carlo). Outputs results to the console.

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