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 Plus

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

Zendy Tools

Zendy Open

A major trend in academia and data science is the rapid adoption of Bayesianstatistics for data analysis and modeling, leading to the development ofprobabilistic programming languages (PPL). A PPL provides a framework thatallows users to easily specify a probabilistic model and perform inferenceautomatically. PyAutoFit is a Python-based PPL which interfaces with allaspects of the modeling (e.g., the model, data, fitting procedure,visualization, results) and therefore provides complete management of everyaspect of modeling. This includes composing high-dimensionality models fromindividual model components, customizing the fitting procedure and performingdata augmentation before a model-fit. Advanced features include database toolsfor analysing large suites of modeling results and exploiting domain-specificknowledge of a problem via non-linear search chaining. Accompanying PyAutoFitis the autofit workspace (see https://github.com/Jammy2211/autofit_workspace),which includes example scripts and the HowToFit lecture series which introducesnon-experts to model-fitting and provides a guide on how to begin a projectusing PyAutoFit. Readers can try PyAutoFit right now by going to theintroduction Jupyter notebook on Binder (seehttps://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD) or checkout ourreadthedocs(see https://pyautofit.readthedocs.io/en/latest/) for a completeoverview of PyAutoFit's features.

PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting