2014/03/31 by Matthew T. Moores, Christopher Drovandi, Christopher C. Drovandi +2
Computer Science · Mathematics · #Algorithm #Approximate Bayesian computation #Artificial intelligence #Bayesian inference #Bayesian probability #Computation #Computer science #Computer vision #Estimation theory #Inference #Likelihood function #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods #Precomputation #Scalability #Smoothing #Target Tracking and Data Fusion in Sensor Networks #acm:62F15 #msc:62F15 #stat.CO
paper · pdf · doi:10.1007/s11222-014-9525-6
5th IMS-ISBA joint meeting (MCMSki IV)
arxiv created 2014/09/05 · openalex publication_date 2014/12/08 · arxiv updated 2014/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.