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/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

"exoplanet" is a toolkit for probabilistic modeling of astronomical timeseries data, with a focus on observations of exoplanets, using PyMC3 (Salvatieret al., 2016). PyMC3 is a flexible and high-performance model-building languageand inference engine that scales well to problems with a large number ofparameters. "exoplanet" extends PyMC3's modeling language to support many ofthe custom functions and probability distributions required when fittingexoplanet datasets or other astronomical time series. While it has been usedfor other applications, such as the study of stellar variability, the primarypurpose of "exoplanet" is the characterization of exoplanets or multiple starsystems using time-series photometry, astrometry, and/or radial velocity. Inparticular, the typical use case would be to use one or more of these datasetsto place constraints on the physical and orbital parameters of the system, suchas planet mass or orbital period, while simultaneously taking into account theeffects of stellar variability.

exoplanet: Gradient-based probabilistic inference for exoplanet data & other astronomical time series