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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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Oskar Høgberg Simensen (2026) studied this question.
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