Moment closure

In probability theory, moment closure is an approximation method used to estimate moments of a stochastic process.^[1] Typically, differential equations describing the ith moment will depend on the (i + 1)th 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]

History

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]

Applications

The approximation has been used successfully to model the spread of the Africanized bee in the Americas^[5] and nematode infection in ruminants.^[6]

References

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