PolyChord can fit models to data in high dimensional scenarios where the data problem is complex and there are many variables. This is its key advantage over any other tool.
PolyChord is a piece of data science software. It fits models to data, acting as an alternative to optimisation tools or Markov-Chain Monte-Carlo approaches. PolyChord is a software tool for use by data scientists in commercial, industrial and scientific research and development departments.
A generalised data science problem can be broken down into stages:
1. Gather and curate your data.
2. Construct models (or refine existing ones) for describing your data.
3. Fit/train these models.
4. Select the best model.
5. Use the model to make predictions.
PolyChord provides a cutting-edge solution to steps 3 and 4. PolyChord represents the cutting-edge in nested sampling. It fits models to data using a Bayesian-inspired sampling approach, and allows for model comparison by computing the marginalised likelihood (Bayesian Evidence).
Crucially, using this methodology PolyChord can fit models to data in high dimensional scenarios where the data problem is complex and there are many variables. This is its key advantage over any other tool, and where it represents a step change forwards for getting information from data.
Numerical modelling (in a broad sense) is the integral part of how we understand and use data. In order to extract information from data, one first constructs a model. One then ‘fits’ or ‘trains’ the model before using the model to extract information about the data, and make further predictions. Modelling acts to compress big data into a manageable and usable tool. Models come in many flavours, the two broad classes being Generative and Discriminative. Scientific models tend to be generative, whilst machine learning models (such as neural networks) tend to be discriminative. Both types however require model fitting or training. PolyChord has competitive advantages in both of these areas. PolyChord is a black-box optimisation tool, as it self-tunes and requires minimal user intervention. PolyChord represents the cutting edge of evidence computation for model comparison, providing more accurate and reliable answers than all existing tools in an achievable computational timescale. PolyChord does not require gradients, merely the optimisation function to be explored, and the region to explore it in. PolyChord is supported by several of the world-experts in the field of nested sampling.