Stochastic modeling is a mathematical approach that incorporates randomness to simulate and analyze complex systems with inherent uncertainty.A method for studying complex systems by constructing a mathematical model that includes random variables and probabilistic transitions, and then numerically reproducing it many times (Monte Carlo method). Stochastic modeling is indispensable when the analytical solution of equations is too cumbersome or unknown, but the statistical properties of elementary events are available. It is used in physics, biology, finance, and epidemiology for risk and uncertainty assessment.
The formal foundations were laid in the early 20th century by the works of A. A. Markov, A. Einstein (Brownian motion), and N. Wiener. The Monte Carlo method was invented by Stanislaw Ulam and John von Neumann in the 1940s for the Manhattan Project. With the growth of computers, stochastic modeling became a standard tool in all sciences, including climatology (ensemble forecasts) and computational biology.
A probabilistic model is defined: for example, the SDE dX_t = μ(X_t,t)dt + σ(X_t,t)dW_t, where W_t is a Wiener process (a continuous random process with independent normal increments). A numerical scheme (Euler–Maruyama) generates discrete increments by adding Gaussian white noise. By repeating the simulation N times, we obtain N trajectories, from which histograms, confidence intervals, etc. are constructed. The convergence of the method is on the order of 1/√N.
Predicting the spread of infections, modeling crowd movement, calculating power plant reliability.
Physicists already in the mid-20th century modeled the movement of neutrons in a nuclear reactor using random numbers — this is how the Monte Carlo method appeared, named after the casino.
| P(D|θ) | variable | probability of data given parameters — probability |
| y_model | variable | model prediction — value |
| y_data | variable | observed data point — value |
| σ | variable | standard deviation of Gaussian noise — standard deviation |
| log | operator | natural logarithm — logarithm |
| ∑ | operator | sum over all data points — summation |
| f(x_i) | variable | model prediction at input x_i — function value |
| y_i | variable | observed data point — data value |
| σ_i | variable | standard deviation of measurement error — standard deviation |
| ∏ | operator | Product over all data points i — product operator |
| exp | operator | Exponential function — exponential function |














