80 citations · 214 across the 10 of their papers we have counts for
5 papers · 1 filter
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
Niccolò Dalmasso, Taylor Pospisil, Ann B. Lee +3
It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Si…
(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data
Taylor Pospisil, Ann B. Lee
Random forests is a common non-parametric regression technique which performs well for mixed-type unordered data and irrelevant features, while being robust to monotonic variable t…
Going Deep: Models for Continuous-Time Within-Play Valuation of Game Outcomes in American Football with Tracking Data
Ronald Yurko, Francesca Matano, Lee F. Richardson +4
Continuous-time assessments of game outcomes in sports have become increasingly common in the last decade. In American football, only discrete-time estimates of play value were pos…
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
Niccolò Dalmasso, Ann B. Lee, Rafael Izbicki +3
Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist…
Non-Gaussianity in the Weak Lensing Correlation Function Likelihood -- Implications for Cosmological Parameter Biases
Chien-Hao Lin, Joachim Harnois-Déraps, Tim Eifler +4
We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in one-…