  Noise-contrastive estimation (NCE) is a popular method for estimating
unnormalised probabilistic models, such as energy-based models, which are
effective for modelling complex data distributions. Unlike classical maximum
likelihood (ML) estimation that relies on importance sampling (resulting in
ML-IS) or MCMC (resulting in contrastive divergence, CD), NCE uses a proxy
criterion to avoid the need for evaluating an often intractable normalisation
constant.
  Despite apparent conceptual differences, we show that two NCE criteria,
ranking NCE (RNCE) and conditional NCE (CNCE), can be viewed as ML estimation
methods. Specifically, RNCE is equivalent to ML estimation combined with
conditional importance sampling, and both RNCE and CNCE are special cases of
CD. These findings bridge the gap between the two method classes and allow us
to apply techniques from the ML-IS and CD literature to NCE, offering several
advantageous extensions.
