Discrete choice models require more information to estimate and more computational time.

This means that the storage requirements and computational time will tend to grow according to the square of the problem size.

For systems with a large parameter space the need for computational time might become significant.

In this case, the forward algorithm can lead to huge computational times.

A direct measure of the computational time required by the algorithms is of much interest.

In our simplified model we use the following variance reduction technique to reduce computational time.

The computational time only depends linearly on the number of operations performed.

The major drawback of the ratio method is increased computational time.

In turn, this number is purely a matter of computational time but does not require any extension of the observed data.

There are two basic approximation schemes to decrease the computational time for such simulations.