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Probabilistic Inference on Noisy Time Series
Hi @ben18785 and @MichaelClerx , I’ve spent the last 2 and a bit weeks looking at the BFGS and L-BFGS/ LM-BFGS algorithms for issue #1083 . The L-BFGS part is...
See #772 Includes: 1. Method for Covariance-Adaptive Slice Sampling: Covariance Matching. 2. Tests for Covariance Matching method. 3. TO DO: Notebooks.
See #772 Includes: 1. Hyperrectangles-based methods for Slice Sampling: Adaptive and Non-Adaptive. Both methods are in pints/pints/_mcmc/_slice_hyperrectangles.py. 2. Tests for Hyperrectangles-based methods. 3. Notebooks for Hyperrectangles-based methods.
https://pints.readthedocs.io/en/stable/error_measures.html Plus add note saying which methods should be implemented (`__call__` and `n_parameters`)
A censored data point is known to fall in a certain interval, but its actual location within that interval is unknown. In time series analysis partially left-censored data often arises...
At the moment the series of intermediate distributions are different between SMC and populationMCMC. We should allow each to have the other's option and make the default the same between...
For an electrochemistry project I would like to try a kalman filter to estimate a model parameter that varies over time. I would like to incorporate this into pints by...
Not sure how easy this is to do in Pints but will likely be very useful for bigger models, with correlated parameters.