Improved numerical efficiency of the leapfrog update in HamiltonianChain. Fixed some errors so that mass-scaling in HamiltonianChain now works correctly, and renamed the inv_mass keyword argument to inverse_mass. Improved input validation for EnsembleSampler. The inference.gp and inference.mcmc modules were becoming too large, and have now been refactored into sub-packages. Support for importing from the old module names inference.gp_tools and inference.pdf_tools has been removed. The current names inference.gp and inference.pdf should be used instead
Fixed various bugs that appeared when testing after updating dependencies to numpy 1.15.0, scipy 1.3...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
In Ref. (GAMBIT Collaboration: Athron et. al., Eur. Phys. J. C. arXiv:1705.07908, 2017) we introduce...
Added a WhiteNoise covariance function to model the presence of Gaussian noise on input data for Gau...
This release contains significant improvements to the GpRegressor class, including: A new option to...
Three new modules have been added to support the construction of likelihood, prior and posterior dis...
Added a new class GpLinearInverter for performing Gaussian-process linear inversion. Added a new cov...
Minor release. Bug fixes: Fixed bug with metric adaptation due to momentum not being resampled. Pre...
This release includes significant improvements to package documentation, which is now also hosted on...
Fixed a bug in the ChangePoint covariance kernel which was causing GpRegressor to incorrectly assess...
New features and efficiency improvements Added framework for adaptation of transition parameters vi...
Added new set of Jupyter notebook demos, which can be found in the /demos/ directory Added a new fu...
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Rather than assuming the mean of the Gaussian process is zero, GpRegressor now treats the mean as a ...
PyMC 4.0.0b1 ⚠ This is the first beta of the next major release for PyMC 4.0.0 (formerly PyMC3). 4.0...
Fixed various bugs that appeared when testing after updating dependencies to numpy 1.15.0, scipy 1.3...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
In Ref. (GAMBIT Collaboration: Athron et. al., Eur. Phys. J. C. arXiv:1705.07908, 2017) we introduce...
Added a WhiteNoise covariance function to model the presence of Gaussian noise on input data for Gau...
This release contains significant improvements to the GpRegressor class, including: A new option to...
Three new modules have been added to support the construction of likelihood, prior and posterior dis...
Added a new class GpLinearInverter for performing Gaussian-process linear inversion. Added a new cov...
Minor release. Bug fixes: Fixed bug with metric adaptation due to momentum not being resampled. Pre...
This release includes significant improvements to package documentation, which is now also hosted on...
Fixed a bug in the ChangePoint covariance kernel which was causing GpRegressor to incorrectly assess...
New features and efficiency improvements Added framework for adaptation of transition parameters vi...
Added new set of Jupyter notebook demos, which can be found in the /demos/ directory Added a new fu...
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Rather than assuming the mean of the Gaussian process is zero, GpRegressor now treats the mean as a ...
PyMC 4.0.0b1 ⚠ This is the first beta of the next major release for PyMC 4.0.0 (formerly PyMC3). 4.0...
Fixed various bugs that appeared when testing after updating dependencies to numpy 1.15.0, scipy 1.3...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
In Ref. (GAMBIT Collaboration: Athron et. al., Eur. Phys. J. C. arXiv:1705.07908, 2017) we introduce...