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Found 6 papers, 5 papers with code
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data
Furthermore, in settings with heteroskedasticity or multimodality, a regression point estimate with standard errors do not fully capture the uncertainty in our predictions.
Computation Methodology
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters.
Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings
In soccer, perhaps the most comprehensive player value statistics come from video games, and in particular FIFA.
Applications
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
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 using a forward model.
RFCDE: Random Forests for Conditional Density Estimation
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 transformations.
