BayesDensity.jl: Bayesian nonparametric density estimation in Julia

BayesDensity.jl is a Julia package for Bayesian nonparametric estimation of smooth univariate densities. The package provides a unified interface to a range of well-established models from the statistical literature, supporting both Markov chain Monte Carlo and variational inference for most implemented procedures. Tailor-made inference algorithms facilitate computational efficiency across all supported models. The practical usage of the package is illustrated through several real-data examples, demonstrating its ability to produce smooth, interpretable density estimates with principled uncertainty quantification. The excellent computational performance of the package is demonstrated in two benchmarks.

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Publication Details

Journal
SoftwareX
Published
2026-06-29
DOI
https://doi.org/10.1016/j.softx.2026.102847
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
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article

BayesDensity.jl: Bayesian nonparametric density estimation in Julia

Oskar Høgberg Simensen
SoftwareX
Markov Chains and Monte Carlo Methods
article

BayesDensity.jl: Bayesian nonparametric density estimation in Julia

Oskar Høgberg Simensen
article en

Abstract

BayesDensity.jl is a Julia package for Bayesian nonparametric estimation of smooth univariate densities. The package provides a unified interface to a range of well-established models from the statistical literature, supporting both Markov chain Monte Carlo and variational inference for most implemented procedures. Tailor-made inference algorithms facilitate computational efficiency across all supported models. The practical usage of the package is illustrated through several real-data examples, demonstrating its ability to produce smooth, interpretable density estimates with principled uncertainty quantification. The excellent computational performance of the package is demonstrated in two benchmarks.

SoftwareXVol. 35
Karlsruhe University of Education (DE)
Openalex Percentile: Top 6%
Markov Chains and Monte Carlo Methods
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