PyVBMC: Efficient Bayesian inference in Python
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Huggins, B., Li, C., Tobaben, M., Aarnos, M. J., & Acerbi, L. (2023). PyVBMC: Efficient Bayesian inference in Python (Version v1.0.1). Zenodo. https://doi.org/10.5281/ZENODO.7966315
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This upload archives the v1.0.1 release of PyVBMC, as prepared for submission to the Journal of Open Source Software. PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for *black-box* computational models. VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or more) and/or noisy.
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Bayesian inference, machine learning, probabilistic modeling, computational statistics