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...
Những tác giả chính: | Clerx, M, Robinson, M, Lambert, B, Lei, C, Ghosh, S, Mirams, G, Gavaghan, D |
---|---|
Định dạng: | Journal article |
Được phát hành: |
Ubiquity Press
2019
|
Những quyển sách tương tự
-
Probabilistic Inference on Noisy Time Series (PINTS)
Bằng: Michael Clerx, et al.
Được phát hành: (2019-07-01) -
Filter inference: a scalable nonlinear mixed effects inference approach for snapshot time series data
Bằng: Augustin, D, et al.
Được phát hành: (2023) -
Ageing Men and Therapeutic Pints in Roddy Doyle’s Two Pints
Bằng: Burcu Gülüm Tekin
Được phát hành: (2017-03-01) -
The First Pint of Science Festival in Asia
Bằng: Robinson, M, et al.
Được phát hành: (2017) -
Evaluation of the Pint of Science festival in Thailand
Bằng: Adhikari, B, et al.
Được phát hành: (2019)