  The sequential dependence model (SDM) is a popular retrieval model which is
based on the theory of probabilistic graphical models. While it was originally
introduced by Metzler and Croft as a Markov Random Field (aka discriminative
probabilistic model), in this paper we demonstrate that it is equivalent to a
generative probabilistic model.
  To build an foundation for future retrieval models, this paper details the
axiomatic underpinning of the SDM model as discriminative and generative
probabilistic model. The only difference arises whether model parameters are
estimated in log-space or Multinomial-space. We demonstrate that
parameter-estimation with grid-tuning is negatively impacting the generative
formulation, an effect that vanishes when parameters are estimated with
coordinate-gradient descent. This is concerning, since empirical differences
may be falsely attributed to improved models.
