Category: Stochastic simulation

Simulation language
A computer simulation language is used to describe the operation of a simulation on a computer. There are two major types of simulation: continuous and discrete event though more modern languages can
Monte Carlo method
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use rando
Network traffic simulation
Network traffic simulation is a process used in telecommunications engineering to measure the efficiency of a communications network.
Evacuation simulation
Evacuation simulation is a method to determine evacuation times for areas, buildings, or vessels. It is based on the simulation of crowd dynamics and pedestrian motion. The distinction between buildin
Gard model
In evolutionary biology, the GARD (Graded Autocatalysis Replication Domain) model is a general kinetic model for homeostatic-growth and fission of compositional-assemblies, with specific application t
In probability theory, tau-leaping, or τ-leaping, is an approximate method for the simulation of a stochastic system. It is based on the Gillespie algorithm, performing all reactions for an interval o
Multilevel Monte Carlo method
Multilevel Monte Carlo (MLMC) methods in numerical analysis are algorithms for computing expectations that arise in stochastic simulations. Just as Monte Carlo methods, they rely on repeated random sa
Discrete-event simulation
A discrete-event simulation (DES) models the operation of a system as a (discrete) sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the syst
Hybrid stochastic simulation
Hybrid stochastic simulations are a sub-class of stochastic simulations. These simulations combine existing stochastic simulations with other stochastic simulations or algorithms. Generally they are u
Stochastic process rare event sampling
Stochastic Process Rare Event Sampling (SPRES) is a Rare Event Sampling method in computer simulation, designed specifically for non-equilibrium calculations, including those for which the rare-event
Multi-state modeling of biomolecules
Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behaviour of biological molecules or complexes that can adopt a large number of possible functio
Stochastic simulation
A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. Realizations of these random variables are generated and
Rule-based modeling
Rule-based modeling is a modeling approach that uses a set of rules that indirectly specifies a mathematical model. The rule-set can either be translated into a model such as Markov chains or differen
Kinetic Monte Carlo
The kinetic Monte Carlo (KMC) method is a Monte Carlo method computer simulation intended to simulate the time evolution of some processes occurring in nature. Typically these are processes that occur
Stochastic roadmap simulation
For robot control, Stochastic roadmap simulation is inspired by probabilistic roadmap methods (PRM) developed for robot motion planning. The main idea of these methods is to capture the connectivity o
Rare event sampling
Rare event sampling is an umbrella term for a group of computer simulation methods intended to selectively sample 'special' regions of the dynamic space of systems which are unlikely to visit those sp
Importance sampling
Importance sampling is a Monte Carlo method for evaluating properties of a particular distribution, while only having samples generated from a different distribution than the distribution of interest.
Probability management
The discipline of probability management communicates and calculates uncertainties as data structures that obey both the laws of arithmetic and probability. The simplest approach is to use vector arra
Particle filter
Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering
Gillespie algorithm
In probability theory, the Gillespie algorithm (or the Doob-Gillespie algorithm or Stochastic Simulation Algorithm, the SSA) generates a statistically correct trajectory (possible solution) of a stoch
Mean-field particle methods
Mean-field particle methods are a broad class of interacting type Monte Carlo algorithms for simulating from a sequence of probability distributions satisfying a nonlinear evolution equation. These fl
Systems simulation
Computers are used to generate numeric models for the purpose of describing or displaying complex interaction among multiple variables within a system. The complexity of the system arises from the sto