Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distance-learning For Approximate Bayesian Computation To Model a
  Volcanic Eruption   Approximate Bayesian computation (ABC) provides us with a way to infer
parameters of models, for which the likelihood function is not available, from
an observation. Using ABC, which depends on many simulations from the
considered model, we develop an inferential framework to learn parameters of a
stochastic numerical simulator of volcanic eruption. Moreover, the model itself
is parallelized using Message Passing Interface (MPI). Thus, we develop a
nested-parallelized MPI communicator to handle the expensive numerical model
with ABC algorithms. ABC usually relies on summary statistics of the data in
order to measure the discrepancy model output and observation. However,
informative summary statistics cannot be found for the considered model. We
therefore develop a technique to learn a distance between model outputs based
on deep metric-learning. We use this framework to learn the plume
characteristics (eg. initial plume velocity) of the volcanic eruption from the
tephra deposits collected by field-work associated with the 2450 BP Pululagua
(Ecuador) volcanic eruption.

3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol