T. Pospisil

Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference

Ann B. Lee, P. Freeman, A. Malz, 2019, Astron. Comput..

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Rafael Izbicki, Ann B. Lee, Taylor Pospisil, 2019, AISTATS.

ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations

Ann B. Lee, Rafael Izbicki, Taylor Pospisil, 2018, Journal of Computational and Graphical Statistics.

Augmenting adjusted plus-minus in soccer with FIFA ratings

Lee F. Richardson, C. Politsch, T. Pospisil, 2018, Journal of Quantitative Analysis in Sports.

(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data

Ann B. Lee, Taylor Pospisil, T. Pospisil, 2019, 1906.07177.

Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)

Ann B. Lee, A. Connolly, P. Freeman, 2020, Monthly Notices of the Royal Astronomical Society.

Non-Gaussianity in the weak lensing correlation function likelihood – implications for cosmological parameter biases

Ann B. Lee, R. Mandelbaum, T. Eifler, 2019, Monthly Notices of the Royal Astronomical Society.