80 citations · 214 across the 10 of their papers we have counts for
10 papers
A Grammar of Data Analysis
Xunmo Yang, Taylor Pospisil, Omkar Muralidharan +1
This paper outlines a grammar of data analysis, as distinct from grammars of data manipulation, in which the primitives are metrics and dimensions. We describe a Python implementat…
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
S. J. Schmidt, A. I. Malz, J. Y. H. Soo +24
Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) poster…
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-…
Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings
Francesca Matano, Lee F. Richardson, Taylor Pospisil +2
In basketball and hockey, state-of-the-art player value statistics are often variants of Adjusted Plus-Minus (APM). But APM hasn't had the same impact in soccer, since soccer games…
RFCDE: Random Forests for Conditional Density Estimation
Taylor Pospisil, Ann B. Lee
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transform…
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Rafael Izbicki, Ann B. Lee, Taylor Pospisil
Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings…