> Hi, > > Thanks for this - yes I think I see that now. (The values do indeed > differ by n_dim * n_samples * log(scale), but no 0.5 here.) > > I guess in a way the issue is that we typically evaluate point > likelihoods, rather than e.g. integrals within some bounds of certainty > of the measurement. If doing the latter, then the size of that 'box' > would also vary with my scaling factor, and should compensate. Note sure I get your point: the expectancy of the log likelihood (i.e. the negative differential entropy) also scales linearly with the dilation factor (indeed without the 1/2). However, this has little impact in e.g. model selection problems, since the global scaling factor is fixed with the data, and thus is the same for all models tested.
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