Probabilistic Inference on Noisy Time Series (PINTS)

Clerx, Michael; Robinson, Martin; Lambert, Ben; Lei, Chon Lok; Ghosh, Sanmitra; Mirams, Gary R.; Gavaghan, David J.

Abstract

Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system’s behaviour changes over time. A key problem in time series modelling is inference; determining properties of the underlying system based on observed time series. For both statistical and mechanistic models, inference involves finding parameter values, or distributions of parameters values, which produce outputs consistent with observations. A wide variety of inference techniques are available and different approaches are suitable for different classes of problems. This variety presents a challenge for researchers, who may not have the resources or expertise to implement and experiment with these methods. PINTS (Probabilistic Inference on Noisy Time Series — https://github.com/pints-team/pints) is an open-source (BSD 3-clause license) Python library that provides researchers with a broad suite of non-linear optimisation and sampling methods. It allows users to wrap a model and data in a transparent and straightforward interface, which can then be used with custom or pre-defined error measures for optimisation, or with likelihood functions for Bayesian inference or maximum-likelihood estimation. Derivative-free optimisation algorithms — which work without harder-to-obtain gradient information — are included, as well as inference algorithms such as adaptive Markov chain Monte Carlo and nested sampling, which estimate distributions over parameter values. By making these statistical techniques available in an open and easy-to-use framework, PINTS brings the power of these modern methods to a wider scientific audience.

Citation

Clerx, M., Robinson, M., Lambert, B., Lei, C. L., Ghosh, S., Mirams, G. R., & Gavaghan, D. J. (2019). Probabilistic Inference on Noisy Time Series (PINTS). Journal of Open Research Software, 7(1), 23. https://doi.org/10.5334/jors.252

• Journal Article Type: Article
• Acceptance Date: Jul 5, 2019
• Online Publication Date: Jul 19, 2019
• Publication Date: Jul 19, 2019
• Deposit Date: Jul 20, 2019
• Publicly Available Date: Jul 23, 2019
• Journal: Journal of Open Research Software
• Electronic ISSN: 2049-9647
• Publisher: Ubiquity Press
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Pages: 23
• DOI: https://doi.org/10.5334/jors.252
• Keywords: Time series models; Non-linear optimisation; MCMC sampling; Nested sampling; Bayesian inference; Python
• Public URL: https://nottingham-repository.worktribe.com/output/2324505
• Publisher URL: https://openresearchsoftware.metajnl.com/articles/10.5334/jors.252/
• Contract Date: Jul 20, 2019