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

Bayesian estimation of trend components within Markovian  regime-switching models for wholesale electricity prices: an application to  the South Australian wholesale electricity market