An ABC interpretation of the multiple auxiliary variable method
arXiv:1604.08102v11 citations
Originality Synthesis-oriented
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This work offers a reinterpretation of an existing method for researchers in statistical inference, but it is incremental as it does not introduce new techniques or applications.
The paper tackles the problem of interpreting the auxiliary variable method for Markov random field inference by showing it can be viewed as an approximate Bayesian computation method for likelihood estimation, providing a new theoretical connection without presenting concrete numerical results.
We show that the auxiliary variable method (Møller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation method for likelihood estimation.