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priorsense: Prior (and likelihood) diagnostics and sensitivity analysis

Source: R/priorsense-package.R
priorsense-package.Rd

The priorsense package provides functions for prior and likelihood sensitivity analysis of Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood and is the first implementation of the method described in Kallioinen et al. (2023).

Details

The main diagnostic function provided by priorsense is powerscale_sensitivity. Given a fitted model or draws object, it computes the powerscaling sensitivity diagnostic described in Kallioinen et al. (2023). It does so by perturbing the prior and likelihood and computing the effect on the posterior, without needing to refit the model (using Pareto smoothed importance sampling and importance weighted moment matching; Vehtari et al. 2022, Paananen et al. 2021).

In addition, visual diagnostics are available by first using powerscale_sequence to create a sequence of perturbed posteriors, and then a plot function such as powerscale_plot_ecdf to visualise the change.

The following global options are available:

  • priorsense.plot_help_text: If TRUE (the default), priorsense plots will include a title and explanatory text. If FALSE they will not.

  • priorsense.plot_variables_per_page: Number specifying the maximum number of variables to be plotted on one page of a plot.

  • priorsense.plot_ask: If TRUE (the default), when multiple pages are plotted input is required before each subsequent page is rendered. If FALSE no input is required.

References

Kallioinen, N., Paananen, T., Bürkner, P-C., Vehtari, A. (2023). Detecting and diagnosing prior and likelihood sensitivity with power-scaling perturbations. Statistics and Computing. 34(57). doi:10.1007/s11222-023-10366-5

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research. 25(72). https://jmlr.org/papers/v25/19-556.html

Paananen, T., Piironen, J., Bürkner, P-C., Vehtari, A. (2021). Implicitly adaptive importance sampling. Statistics and Computing. 31(16). doi:10.1007/s11222-020-09982-2

See also

powerscale_sensitivity powerscale_sequence powerscale powerscale_plot_ecdf powerscale_plot_dens powerscale_plot_quantities

Author

Maintainer: Noa Kallioinen noa.kallioinen@aalto.fi [copyright holder]

Authors:

  • Topi Paananen

  • Paul-Christian Bürkner

  • Aki Vehtari

Other contributors:

  • Frank Weber [contributor]