A central use of the PolyChord tool is solving problems presented by high-dimensional data. High-dimensional data is present for many businesses in today’s world of BigData, where data has been gathered and there are many many variables. It is analysed in an attempt to create data models and formulas. If an accurate model is produced then the model can be used to make accurate future predictions. However, creating a model to fit high-dimensional data has been, until now, a notoriously difficult task.
This article by Sunil Sapra discusses the use of high-dimensional data and where it occurs in common commercial scenarios, and is a great explanation of the difficulties that come with fitting a model to high-dimensional data.
Here at PolyChord, we have been able to overcome a majority of these difficulties by using advanced Bayesian Data Science techniques. Unlike other tools, PolyChord takes an orthogonal approach using nested sampling to navigate thousands of dimensions and compute without compromises or approximation.
****Disclaimer: Dr. Sapra is not associated with the PolyChord organisation and has kindly given us permission to use his well written paper****