An Extendable Python Implementation of Robust Optimization Monte Carlo
Dublin Core
Title
An Extendable Python Implementation of Robust Optimization Monte Carlo
Subject
Bayesian inference, implicit models, likelihood-free, Python, elf
Description
Performing inference in statistical models with an intractable likelihood is challenging,
therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency
limitations. In this paper, we present the implementation of the LFI method robust
optimization Monte Carlo (ROMC) in the Python package elfi. ROMC is a novel and
efficient (highly-parallelizable) LFI framework that provides accurate weighted samples
from the posterior. Our implementation can be used in two ways. First, a scientist may
use it as an out-of-the-box LFI algorithm; we provide an easy-to-use API harmonized with
the principles of elfi, enabling effortless comparisons with the rest of the methods included
in the package. Additionally, we have carefully split ROMC into isolated components for
supporting extensibility. A researcher may experiment with novel method(s) for solving
part(s) of ROMC without reimplementing everything from scratch. In both scenarios, the
ROMC parts can run in a fully-parallelized manner, exploiting all CPU cores. We also
provide helpful functionalities for (i) inspecting the inference process and (ii) evaluating
the obtained samples. Finally, we test the robustness of our implementation on some
typical LFI examples.
therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency
limitations. In this paper, we present the implementation of the LFI method robust
optimization Monte Carlo (ROMC) in the Python package elfi. ROMC is a novel and
efficient (highly-parallelizable) LFI framework that provides accurate weighted samples
from the posterior. Our implementation can be used in two ways. First, a scientist may
use it as an out-of-the-box LFI algorithm; we provide an easy-to-use API harmonized with
the principles of elfi, enabling effortless comparisons with the rest of the methods included
in the package. Additionally, we have carefully split ROMC into isolated components for
supporting extensibility. A researcher may experiment with novel method(s) for solving
part(s) of ROMC without reimplementing everything from scratch. In both scenarios, the
ROMC parts can run in a fully-parallelized manner, exploiting all CPU cores. We also
provide helpful functionalities for (i) inspecting the inference process and (ii) evaluating
the obtained samples. Finally, we test the robustness of our implementation on some
typical LFI examples.
Creator
Vasilis Gkolemis
Source
https://www.jstatsoft.org/article/view/v110i02
Publisher
ATHENA RC
Date
August 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Vasilis Gkolemis, “An Extendable Python Implementation of Robust Optimization Monte Carlo,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8338.