flowMC: Normalizing flow enhanced sampling package forprobabilistic inference in JAX Article Swipe

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Kaze W. K. Wong , Marylou Gabrié , Daniel Foreman-Mackey ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.21105/joss.05021

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Markov chain Monte Carlo Metropolis–Hastings algorithm Python (programming language) Computer science Bayesian inference Leverage (statistics) Inference Markov chain Algorithm Posterior probability Sampling (signal processing) Bayesian probability Kernel (algebra) Mathematics Machine learning Artificial intelligence Discrete mathematics Operating system Filter (signal processing) Computer vision

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Type: article
Language: en
Landing Page: https://doi.org/10.21105/joss.05021
PDF: https://joss.theoj.org/papers/10.21105/joss.05021.pdf
OA Status: diamond
Cited By: 22
References: 20
Related Works: 10
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DOI: https://doi.org/10.21105/joss.05021

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Title: flowMC: Normalizing flow enhanced sampling package forprobabilistic inference in JAX

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-03-09

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Authors: Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey

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Landing page: https://doi.org/10.21105/joss.05021

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Concepts: Markov chain Monte Carlo, Metropolis–Hastings algorithm, Python (programming language), Computer science, Bayesian inference, Leverage (statistics), Inference, Markov chain, Algorithm, Posterior probability, Sampling (signal processing), Bayesian probability, Kernel (algebra), Mathematics, Machine learning, Artificial intelligence, Discrete mathematics, Operating system, Filter (signal processing), Computer vision

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Cited by: 22

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Citations by year (recent): 2025: 12, 2024: 6, 2023: 3, 2022: 1

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primary_location.raw_source_name	Journal of Open Source Software
primary_location.landing_page_url	https://doi.org/10.21105/joss.05021
publication_date	2023-03-09
publication_year	2023
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referenced_works_count	20
abstract_inverted_index.audience	1
abstract_inverted_index.International	0
cited_by_percentile_year.max	99
cited_by_percentile_year.min	89
countries_distinct_count	2
institutions_distinct_count	3
citation_normalized_percentile.value	0.95602659
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True