Constraining the equation of state of cold dense matter in compact stars is a major science goal for observing programmes being conducted using X-ray, radio, and gravitational wave telescopes. We discuss Bayesian hierarchical inference of parametrised dense matter equations of state. In particular we generalise and examine two inference paradigms from the literature: (i) direct posterior equation of state parameter estimation, conditioned on observations of a set of rotating compact stars; and (ii) indirect parameter estimation, via transformation of an intermediary joint posterior distribution of exterior spacetime parameters (such as gravitational masses and coordinate equatorial radii). We conclude that the former paradigm is not only tractable for large-scale analyses, but is principled and flexible from a Bayesian perspective whilst the latter paradigm is not. The thematic problem of Bayesian prior definition emerges as the crux of the difference between these paradigms. The second paradigm should in general only be considered as an ill-defined approach to the problem of utilising archival posterior constraints on exterior spacetime parameters; we advocate for an alternative approach whereby such information is repurposed as an approximative likelihood function. We also discuss why conditioning on a piecewise-polytropic equation of state model -currently standard in the field of dense matter study -can easily violate conditions required for transformation of a probability density distribution between spaces of exterior (spacetime) and interior (source matter) parameters.3 Note that, as stated above, one important caveat regards the assumption of BNS components sharing the same cold EOS -during inspiral -as stars which do not exist in compact relativistic binaries. In principle, if there is evidence that model complexity needs to be increased, these two classes of star could be assumed to only share a subset of (continuous, phenomenological) EOS parameters, whilst another subset of EOS parameters are used to emulate temperature dependence. 4 We use bold font to denote both a set of abstract parameters, and a point in the associated parameter space. For instance, θ ∈ T where T ⊂ R n , where the coordinates of point θ represent parameter values. 13 Or, interchangeably, a point in a space of parameters defined as random variables. 14 That is, all elements of X are ordered countably infinite tuples, but each coordinate is restricted to a compact subdomain of R, and a tuple of coordinates is a point in R ∞ .