Paper Info
Title
Sparse Hilbert Embedding-Based Statistical Inference of Stochastic Ecological Systems.
Abstract
The growth rate of a population has been an important aspect in ecological and biologic applications. Since a non-linear stochastic behavior is linked to this type of systems, the inference of the model parameters is a challenging task. Approximate Bayesian Computation (ABC) can be used for leading the intractability of the likelihood function caused by the model characteristics. Recently, some methods based on Hilbert Space Embedding (HSE) have been proposed in the context of ABC; nevertheless, the relevance of the observations and simulations are not contemplated. Here, we develop a Sparse HSE-based distance, termed SHSED, to compare distributions associated with two random variables through sparse estimations of the densities in a Reproducing Kernel Hilbert Space (RKHS). Namely, SHSED highlights relevant information using a sparse weighted representation of data within an ABC-based inference. Our method improves the inference accuracy of a Ricker map-based population model in comparison with other state-of-the-art ABC-based approaches.
Year
Venue
Field
2017
CIARP
Hilbert space,Population,Approximate Bayesian computation,Random variable,Likelihood function,Pattern recognition,Inference,Computer science,Algorithm,Artificial intelligence,Statistical inference,Reproducing kernel Hilbert space
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
3
3
Name
Order
Citations
PageRank
Wilson González-Vanegas101.35
Andrés Marino Álvarez-Meza29316.89
Álvaro Orozco-Gutiérrez352.46