ImportanceSamplingResult

ImportanceSamplingResult(
    self,
    log_weights: Tensor,
    log_norm: Tensor,
    ess: Tensor,
)

An object containing the results of importance sampling.

Attributes

log_weights : Tensor

An \(n\)-dimensional vector containing the unnormalised importance weights associated with a set of samples.

log_norm : Tensor

An estimate of the logarithm of the normalising constant associated with the target density.

ess : Tensor

An estimate of the effective sample size.

Notes

The effective sample size is computed using the formula \[ N_{\mathrm{eff}} = \frac{(\sum_{i=1}^{n}w_{i})^{2}}{\sum_{i=1}^{n}w_{i}^{2}}, \] where \(w_{i}\) denotes the importance weight associated with particle \(i\) (see, e.g., Owen 2013).

References

Owen, Art B. 2013. Monte Carlo Theory, Methods and Examples. https://artowen.su.domains/mc/.