This article focuses only on one specialized aspect of the subject.(June 2023) |
In probability theory, moment closure is an approximation method used to estimate moments of a stochastic process. [1]
Typically, differential equations describing the i-th moment will depend on the (i + 1)-st moment. To use moment closure, a level is chosen past which all cumulants are set to zero. This leaves a resulting closed system of equations which can be solved for the moments. [1] The approximation is particularly useful in models with a very large state space, such as stochastic population models. [1]
The moment closure approximation was first used by Goodman [2] and Whittle [3] [4] who set all third and higher-order cumulants to be zero, approximating the population distribution with a normal distribution. [1]
In 2006, Singh and Hespanha proposed a closure which approximates the population distribution as a log-normal distribution to describe biochemical reactions. [5]
The approximation has been used successfully to model the spread of the Africanized bee in the Americas, [6] nematode infection in ruminants. [7] and quantum tunneling in ionization experiments. [8]
This article focuses only on one specialized aspect of the subject.(June 2023) |
In probability theory, moment closure is an approximation method used to estimate moments of a stochastic process. [1]
Typically, differential equations describing the i-th moment will depend on the (i + 1)-st moment. To use moment closure, a level is chosen past which all cumulants are set to zero. This leaves a resulting closed system of equations which can be solved for the moments. [1] The approximation is particularly useful in models with a very large state space, such as stochastic population models. [1]
The moment closure approximation was first used by Goodman [2] and Whittle [3] [4] who set all third and higher-order cumulants to be zero, approximating the population distribution with a normal distribution. [1]
In 2006, Singh and Hespanha proposed a closure which approximates the population distribution as a log-normal distribution to describe biochemical reactions. [5]
The approximation has been used successfully to model the spread of the Africanized bee in the Americas, [6] nematode infection in ruminants. [7] and quantum tunneling in ionization experiments. [8]