method

Stochastic modeling

Stochastic modeling is a mathematical approach that incorporates randomness to simulate and analyze complex systems with inherent uncertainty.
51 related articles · 2 formulas · 6 who worked here

Description

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.

History

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.

How it works

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.

In practice

Predicting the spread of infections, modeling crowd movement, calculating power plant reliability.

Fun fact

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.

Concept graph →

Formulas

$$\log P(D|\theta) = -\frac{1}{2}\sum \left(\frac{(y_{\text{model}} - y_{\text{data}})^2}{\sigma^2} + \log(2\pi\sigma^2)\right)$$
Probability of data given model parameters with Gaussian errors.
What is in the formula
P(D|θ)variableprobability of data given parameters — probability
y_modelvariablemodel prediction — value
y_datavariableobserved data point — value
σvariablestandard deviation of Gaussian noise — standard deviation
logoperatornatural logarithm — logarithm
∑operatorsum over all data points — summation
\logp(d|θ)=-\frac{1}{2}\sum_{i=1}^n[\frac{(r_{\rmmodel}^{i}-r_{\rmdata}^{i})^2}{σ_{{\rmdata},i}^2+σ_{\rmsys}^2}+\log(2π(σ_{{\rmdata},i}^2+σ_{\rmsys}^2))] · 2607.05510v1
$$\mathcal{L} = \prod_i \exp\left( -\frac{1}{2} \left[ \frac{f(x_i) - y_i}{\sigma_i} \right]^2 \right)$$
Probability of observed data given a model with Gaussian errors.
What is in the formula
f(x_i)variablemodel prediction at input x_i — function value
y_ivariableobserved data point — data value
σ_ivariablestandard deviation of measurement error — standard deviation
∏operatorProduct over all data points i — product operator
expoperatorExponential function — exponential function
\mathcal{l}_{\rmdesi}=\prod_i\exp(-\frac{1}{2}[\frac{f(x_i)-y_i}{σ_i}]) · 2601.06656

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