Efficient recursions for general factorisable models

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Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “Friel and Rue (2007) made use of similar ideas to perform approximate Bayesian inference for factorisable models (in particular, binary Markov random fields) that allow for recursive computing (Bartolucci and Besag, 2002; Reeves and Pettitt, 2004).”
Marginal likelihood estimation via power posteriors (2008, methodology): “…provided that the minimum of the number of rows and the number of columns is not greater than 20 and that the larger of the number of rows and the number of columns does not exceed 50, then the forward recursion method that was presented in Reeves and Pettitt (2004) can be used to calculate c.β/.”
An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants (2006, methodology): “Keywords: Autologistic model; Auxiliary variable method; Hierarchical model; Ising model; Markov chain Monte Carlo; Metropolis-Hastings algorithm; Normalising constant; Partition function; Perfect simulation; Strauss point process.”