Are there canonical datasets for benchmarking the performance of posterior inference algorithms? For example in machine learning literature, the MNIST dataset (and others) is often used in benchmarking optizmation algorithms.

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Which properties are desirable in a dataset and model for a comprehensive assessment of Bayesian posterior inference algorithms?

An example of a desirable property of a dataset and model might be the presence of multiple modes separated by large regions of low density in the posterior. Another example is a high dimensional problem that has been solved.

Looks like StRD MCMC Dataset had a promising start, but the datasets are very small and dated.

Another example is the collection of oddly-shaped, 2-dimensional posterior distributions seen in this HMC visualization application. Are there any known datasets/models with properties exhibited in this example but in higher dimensions? Even better if it has known expected values.

Seems like a similar discussion took place: performance benchmarks for MCMC (@lacerbi).


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