posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms

2024/07/06 by Måns Magnusson, Jakob Torgander, Magnusson, Måns +9 · 10 citations
Computer Science · #Anomaly Detection Techniques and Applications #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2407.04967

openalex publication_date 2024/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When designing a new general-purpose inference algorithm, whether it involves Monte Carlo sampling or variational approximation, the fundamental problem arises in evaluating its accuracy and efficiency across a range of representative target models. To solve this problem, we propose posteriordb, a database of models and data sets defining target densities along with reference Monte Carlo draws. We further provide a guide to the best practices in using posteriordb for model evaluation and comparison. To provide a wide range of realistic target densities, posteriordb currently comprises 120 representative models and has been instrumental in developing several general inference algorithms.

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