Park, M; Jitkrittum, W; Sejdinovic, D; (2016) K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. In: Gretton, A and Robert, CC, (eds.) Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (pp. pp. 398-407). Journal of Machine Learning Research (JMLR): Cadiz, Spain.

Preview

Text Jikrittum_park16.pdf Download (1MB) | Preview

Abstract

Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a parameter value is relatively easy. Approximate Bayesian Computation (ABC) is a paradigm that enables simulation-based posterior inference in such cases by measuring the similarity between simulated and observed data in terms of a chosen set of summary statistics. However, there is no general rule to construct sufficient summary statistics for complex models. Insufficient summary statistics will leak information, which leads to ABC algorithms yielding samples from an incorrect posterior. In this paper, we propose a fully nonparametric ABC paradigm which circumvents the need for manually selecting summary statistics. Our approach, K2-ABC, uses maximum mean discrepancy (MMD) to construct a dissimilarity measure between the observed and simulated data. The embedding of an empirical distribution of the data into a reproducing kernel Hilbert space plays a role of the summary statistic and is sufficient whenever the corresponding kernels are characteristic. Experiments on a simulated scenario and a real-world biological problem illustrate the effectiveness of the proposed algorithm.

Download activity - last month

Export as CSVJSONXML

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as XMLJSONCSV

Archive Staff Only

View Item