Pre-processing for approximate Bayesian computation in image analysis

Understand

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration.

• However, the computational cost of these simulations can be prohibitive for high dimensional data.
• An important example is the Potts model, which is commonly used in image analysis.
• Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern.