https://github.com/bouchardlab/orthogonal-stochastic-linear-mixing-model
This is the python implementation of the paper [Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model]. We propose a new regression framework to model multivariate output response data, which not only capture the complex input-dependent correlation across outputs, but also is effient for massive model and capable for single-trial analysis in neural data. Please refer our model for more details.
https://github.com/bouchardlab/orthogonal-stochastic-linear-mixing-model
Last synced: 4 months ago
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This is the python implementation of the paper [Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model]. We propose a new regression framework to model multivariate output response data, which not only capture the complex input-dependent correlation across outputs, but also is effient for massive model and capable for single-trial analysis in neural data. Please refer our model for more details.
- Host: GitHub
- URL: https://github.com/bouchardlab/orthogonal-stochastic-linear-mixing-model
- Owner: BouchardLab
- License: mit
- Created: 2021-06-03T00:03:13.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-06-03T00:06:18.000Z (about 5 years ago)
- Last Synced: 2025-09-09T22:39:45.797Z (9 months ago)
- Language: Python
- Size: 12.6 MB
- Stars: 0
- Watchers: 8
- Forks: 0
- Open Issues: 0