English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

It is well known in astronomy that propagating non-Gaussian predictionuncertainty in photometric redshift estimates is key to reducing bias indownstream cosmological analyses. Similarly, likelihood-free inferenceapproaches, which are beginning to emerge as a tool for cosmological analysis,require a characterization of the full uncertainty landscape of the parametersof interest given observed data. However, most machine learning (ML) ortraining-based methods with open-source software target point prediction orclassification, and hence fall short in quantifying uncertainty in complexregression and parameter inference settings. As an alternative to methods thatfocus on predicting the response (or parameters) $\mathbf{y}$ from features$\mathbf{x}$, we provide nonparametric conditional density estimation (CDE)tools for approximating and validating the entire probability density function(PDF) $\mathrm{p}(\mathbf{y}|\mathbf{x})$ of $\mathbf{y}$ given (i.e.,conditional on) $\mathbf{x}$. As there is no one-size-fits-all CDE method, thegoal of this work is to provide a comprehensive range of statistical tools andopen-source software for nonparametric CDE and method assessment which canaccommodate different types of settings and be easily fit to the problem athand. Specifically, we introduce four CDE software packages in$\texttt{Python}$ and $\texttt{R}$ based on ML prediction methods adapted andoptimized for CDE: $\texttt{NNKCDE}$, $\texttt{RFCDE}$, $\texttt{FlexCode}$,and $\texttt{DeepCDE}$. Furthermore, we present the $\texttt{cdetools}$package, which includes functions for computing a CDE loss function for tuningand assessing the quality of individual PDFs, along with diagnostic functions.We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well asexamples of applications to photometric redshift estimation and likelihood-freecosmological inference via CDE.

Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference