Sparse Hilbert Embedding-Based Statistical Inference of Stochastic Ecological Systems

González-Vanegas, Wilson; Álvarez-Meza, Andrés Marino; Gutiérrez, Álvaro Ángel Orozco

doi:10.1007/978-3-319-75193-1_31

Cited by 3 publications

(1 citation statement)

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“…However, the selection of proper and sufficient summary statistics could be difficult for complex models. This fact has led to the need to explore alternative approaches that rely on kernel functions to embed and compare distributions into a reproducing kernel hilbert space (RKHS) [ 20 , 21 ]. Nonetheless, the techniques mentioned above require the estimation of different parameters related to the similarity computation among simulations to approximate the posterior.…”

Section: Introductionmentioning

confidence: 99%

AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning

González-Vanegas

Álvarez-Meza

Hernández-Muriel

et al. 2019

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Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of ABC-based approaches have appeared in the literature; however, they all share a hard dependence on free parameters selection, demanding expensive tuning procedures. In this paper, we introduce an automatic kernel learning-based ABC approach, termed AKL-ABC, to automatically compute posterior estimations from a weighting-based inference. To reach this goal, we propose a kernel learning stage to code similarities between simulation and parameter spaces using a centered kernel alignment (CKA) that is automated via an Information theoretic learning approach. Besides, a local neighborhood selection (LNS) algorithm is used to highlight local dependencies over simulations relying on graph theory. Attained results on synthetic and real-world datasets show our approach is a quite competitive method compared to other non-automatic state-of-the-art ABC techniques.

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Section: Introductionmentioning

confidence: 99%