  We discuss and extend methods for estimating Markovian-Regime-Switching (MRS)
and trend models for wholesale electricity prices. We argue the existing
methods of trend estimation used in the electricity price modelling literature
either require an ambiguous definition of an extreme price, or lead to issues
when implementing model selection [23]. The first main contribution of this
paper is to design and infer a model which has a model-based definition of
extreme prices and permits the use of model selection criteria. Due to the
complexity of the MRS models inference is not straightforward. In the existing
literature an approximate EM algorithm is used [26]. Another contribution of
this paper is to implement exact inference in a Bayesian setting. This also
allows the use of posterior predictive checks to assess model fit. We
demonstrate the methodologies with South Australian electricity market.
