# vittles: Variational inference tools to leverage estimator sensitivity

Ryan Giordano

GitHub
January 11, 2019

Many data analysis tasks can be represented or approximated as sensitivity analyses, including frequentist covariance estimates, linear response Bayesian covariances, local sensitivity to priors, data, or hyperparameters, approximate leave-k-out estimators, and the infinitesimal jackknife. In the past, sensitivity analysis typically require laborious and error-prone manual computation of derivatives, but with modern automatic differentiation (autograd) and parameter wrapping (paragami), sensitivity analysis can be automated for a wide class of problems. The vittles package provides, at its core, a class for efficiently calculating arbitrarily high-order Taylor series approximations to dependence of the solution of an optimization problem on its hyperparameters, encompassing the above-mentioned applications and more as special cases.

https://github.com/rgiordan/vittles, or just \$ pip install vittles

### Featured Fellows

#### Ryan Giordano

Statistics
BIDS Alum – DATA SCIENCE FELLOW