A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models

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Sample Size Determination for Robust Bayesian Analysis (2006, methodology): “Conversely, a design prior is chosen, using the terminology of Wang and Gelfand (2002), in a "what-if" spirit.”
What Are the Limits of Posterior Distributions Arising From Nonidentified Models, and Why Should We Care? (2009, background): “On the other hand, as stressed by Wang and Gelfand (2002), one may be more comfortable in ascribing a relatively narrow swath of the parameter space as the region where performance is of interest than in providing the same swath as an input to guide the analysis of a given data set.”
Bayesian estimation supersedes the t test. (2013, background): “The issue has been explored in technical detail in various domains (e.g., Adcock, 1997; De Santis, 2004, 2007; Joseph, Wolfson, & du Berger, 1995a, 1995b; Wang & Gelfand, 2002; Weiss, 1997).”
Sample size planning for statistical power and accuracy in parameter estimation. (2008, background): “In the context of Bayesian inference, M’Lan et al. (2006) and Wang & Gelfand (2002) discuss similar a priori Monte Carlo simulation approaches for AIPE and power.”