Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ABC random forests for Bayesian parameter inference   This preprint has been reviewed and recommended by Peer Community In
Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036).
Approximate Bayesian computation (ABC) has grown into a standard methodology
that manages Bayesian inference for models associated with intractable
likelihood functions. Most ABC implementations require the preliminary
selection of a vector of informative statistics summarizing raw data.
Furthermore, in almost all existing implementations, the tolerance level that
separates acceptance from rejection of simulated parameter values needs to be
calibrated. We propose to conduct likelihood-free Bayesian inferences about
parameters with no prior selection of the relevant components of the summary
statistics and bypassing the derivation of the associated tolerance level. The
approach relies on the random forest methodology of Breiman (2001) applied in a
(non parametric) regression setting. We advocate the derivation of a new random
forest for each component of the parameter vector of interest. When compared
with earlier ABC solutions, this method offers significant gains in terms of
robustness to the choice of the summary statistics, does not depend on any type
of tolerance level, and is a good trade-off in term of quality of point
estimator precision and credible interval estimations for a given computing
time. We illustrate the performance of our methodological proposal and compare
it with earlier ABC methods on a Normal toy example and a population genetics
example dealing with human population evolution. All methods designed here have
been incorporated in the R package abcrf (version 1.7) available on CRAN.

1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th