Probabilistic inference on noisy time series (PINTS)
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 stati...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Journal article |
| Published: |
Ubiquity Press
2019
|
| _version_ | 1797058708311638016 |
|---|---|
| author | Clerx, M Robinson, M Lambert, B Lei, C Ghosh, S Mirams, G Gavaghan, D |
| author_facet | Clerx, M Robinson, M Lambert, B Lei, C Ghosh, S Mirams, G Gavaghan, D |
| author_sort | Clerx, M |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T19:54:07Z |
| format | Journal article |
| id | oxford-uuid:24f58bc7-5da1-4b1a-88ce-1e12c9c1e550 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:54:07Z |
| publishDate | 2019 |
| publisher | Ubiquity Press |
| record_format | dspace |
| spelling | oxford-uuid:24f58bc7-5da1-4b1a-88ce-1e12c9c1e5502022-03-26T11:53:07ZProbabilistic inference on noisy time series (PINTS)Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:24f58bc7-5da1-4b1a-88ce-1e12c9c1e550Symplectic Elements at OxfordUbiquity Press2019Clerx, MRobinson, MLambert, BLei, CGhosh, SMirams, GGavaghan, D 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. |
| spellingShingle | Clerx, M Robinson, M Lambert, B Lei, C Ghosh, S Mirams, G Gavaghan, D Probabilistic inference on noisy time series (PINTS) |
| title | Probabilistic inference on noisy time series (PINTS) |
| title_full | Probabilistic inference on noisy time series (PINTS) |
| title_fullStr | Probabilistic inference on noisy time series (PINTS) |
| title_full_unstemmed | Probabilistic inference on noisy time series (PINTS) |
| title_short | Probabilistic inference on noisy time series (PINTS) |
| title_sort | probabilistic inference on noisy time series pints |
| work_keys_str_mv | AT clerxm probabilisticinferenceonnoisytimeseriespints AT robinsonm probabilisticinferenceonnoisytimeseriespints AT lambertb probabilisticinferenceonnoisytimeseriespints AT leic probabilisticinferenceonnoisytimeseriespints AT ghoshs probabilisticinferenceonnoisytimeseriespints AT miramsg probabilisticinferenceonnoisytimeseriespints AT gavaghand probabilisticinferenceonnoisytimeseriespints |