pyLAIS: A Python package for Layered Adaptive Importance Sampling
Monte Carlo (MC) techniques are widely used to draw from complex distributions and/or for the calculation of related integrals. The most famous families of MC methods are Markov Chain Monte Carlo (MCMC) and importance sampling (IS). There are many separate implementations and packages, available onl...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024003467 |
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| author | Ernesto Curbelo Luca Martino David Delgado-Gómez |
| author_facet | Ernesto Curbelo Luca Martino David Delgado-Gómez |
| author_sort | Ernesto Curbelo |
| collection | DOAJ |
| description | Monte Carlo (MC) techniques are widely used to draw from complex distributions and/or for the calculation of related integrals. The most famous families of MC methods are Markov Chain Monte Carlo (MCMC) and importance sampling (IS). There are many separate implementations and packages, available online regarding MCMC or IS methods. Moreover, both techniques present different drawbacks and advantages. In this paper, we introduce a flexible Python implementation of the so-called layered adaptive importance sampling (LAIS) algorithm. LAIS combines the benefits of MCMC and IS schemes: the exploration of the state space by Markov chains and the low variance estimations provides by advanced and modern IS schemes. More precisely, LAIS allows the sampling of complex distributions and/or approximation of integrals involving complex distributions, through the combination of – possibly sophisticated – MCMC schemes and multiple importance sampling (MIS) techniques. In addition, the modular nature of the algorithm itself provides a great flexibility in choosing the desired MCMC techniques, invariant distributions, proposal densities and MIS approaches. |
| format | Article |
| id | doaj-art-7d48c394510d400a939e76e6e2f62082 |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-7d48c394510d400a939e76e6e2f620822025-08-20T02:13:45ZengElsevierSoftwareX2352-71102025-02-012910197610.1016/j.softx.2024.101976pyLAIS: A Python package for Layered Adaptive Importance SamplingErnesto Curbelo0Luca Martino1David Delgado-Gómez2Universidad Carlos III de Madrid, Avenidad Universidad 30 Leganés Madrid, Spain; Corresponding author.Università degli Studi di Catania, corso Italia 55, Catania, ItalyUniversidad Carlos III de Madrid, Avenidad Universidad 30 Leganés Madrid, SpainMonte Carlo (MC) techniques are widely used to draw from complex distributions and/or for the calculation of related integrals. The most famous families of MC methods are Markov Chain Monte Carlo (MCMC) and importance sampling (IS). There are many separate implementations and packages, available online regarding MCMC or IS methods. Moreover, both techniques present different drawbacks and advantages. In this paper, we introduce a flexible Python implementation of the so-called layered adaptive importance sampling (LAIS) algorithm. LAIS combines the benefits of MCMC and IS schemes: the exploration of the state space by Markov chains and the low variance estimations provides by advanced and modern IS schemes. More precisely, LAIS allows the sampling of complex distributions and/or approximation of integrals involving complex distributions, through the combination of – possibly sophisticated – MCMC schemes and multiple importance sampling (MIS) techniques. In addition, the modular nature of the algorithm itself provides a great flexibility in choosing the desired MCMC techniques, invariant distributions, proposal densities and MIS approaches.http://www.sciencedirect.com/science/article/pii/S2352711024003467Monte Carlo methodsImportance samplingBayesian inference |
| spellingShingle | Ernesto Curbelo Luca Martino David Delgado-Gómez pyLAIS: A Python package for Layered Adaptive Importance Sampling SoftwareX Monte Carlo methods Importance sampling Bayesian inference |
| title | pyLAIS: A Python package for Layered Adaptive Importance Sampling |
| title_full | pyLAIS: A Python package for Layered Adaptive Importance Sampling |
| title_fullStr | pyLAIS: A Python package for Layered Adaptive Importance Sampling |
| title_full_unstemmed | pyLAIS: A Python package for Layered Adaptive Importance Sampling |
| title_short | pyLAIS: A Python package for Layered Adaptive Importance Sampling |
| title_sort | pylais a python package for layered adaptive importance sampling |
| topic | Monte Carlo methods Importance sampling Bayesian inference |
| url | http://www.sciencedirect.com/science/article/pii/S2352711024003467 |
| work_keys_str_mv | AT ernestocurbelo pylaisapythonpackageforlayeredadaptiveimportancesampling AT lucamartino pylaisapythonpackageforlayeredadaptiveimportancesampling AT daviddelgadogomez pylaisapythonpackageforlayeredadaptiveimportancesampling |