sampling-importance-resampling is not equivalent to exact sampling [triste SIR]

Following an X validated question on the topic, I reassessed a previous impression I had that sampling-importance-resampling (SIR) is equivalent to direct sampling for a given sample size. (As suggested in the above fit between a N(2,½) target and a N(0,1) proposal.)  Indeed, when one produces a sample

x_1,\ldots,x_n \stackrel{\text{i.i.d.}}{\sim} g(x)

and resamples with replacement from this sample using the importance weights

f(x_1)g(x_1)^{-1},\ldots,f(x_n)g(x_n)^{-1}

the resulting sample

y_1,\ldots,y_n

is neither “i.” nor “i.d.” since the resampling step involves a self-normalisation of the weights and hence a global bias in the evaluation of expectations. In particular, if the importance function g is a poor choice for the target f, meaning that the exploration of the whole support is imperfect, if possible (when both supports are equal), a given sample may well fail to reproduce the properties of an iid example ,as shown in the graph below where a Normal density is used for g while f is a Student t⁵ density:

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