  The present work shows that the maximum-entropy method can be applied to a
sample of neuronal recordings along two different routes: (1) apply to the
sample; or (2) apply to a larger, unsampled neuronal population from which the
sample is drawn, and then marginalize to the sample. These two routes give
inequivalent results. The second route can be further generalized to the case
where the size of the larger population is unknown. Which route should be
chosen? Some arguments are presented in favour of the second. This work also
presents and discusses probability formulae that relate states of knowledge
about a population and its samples, and that may be useful for sampling
problems in neuroscience.
