Added a new class GpLinearInverter for performing Gaussian-process linear inversion. Added a new covariance function class ChangePoint. Hyper-parameter labels for mean and covariance functions have been redesigned to be more specific. GpRegressor has a new keyword argument n_starts which allows the number of BFGS starting positions used during hyper-parameter optimization to be set manually
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Added a display_progress keyword argument for MCMC classes which can be used to suppress progress me...
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and pred...
This release contains significant improvements to the GpRegressor class, including: A new option to...
Fixed a bug in the ChangePoint covariance kernel which was causing GpRegressor to incorrectly assess...
Rather than assuming the mean of the Gaussian process is zero, GpRegressor now treats the mean as a ...
Added a WhiteNoise covariance function to model the presence of Gaussian noise on input data for Gau...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
Added a new method GpRegressor.gradient, which allows for the calculation of the mean and variance o...
GpRegressor now supports multi-start gradient-based hyper-parameter optimisation using the L-BFGS-B ...
Added the HeteroscedasticNoise covariance kernel to the inference.covariance module, which allows fo...
Three new modules have been added to support the construction of likelihood, prior and posterior dis...
Improved numerical efficiency of the leapfrog update in HamiltonianChain. Fixed some errors so that ...
Fixed a bug introduced in the 0.5.0 release where a passing single spatial point to the __call__ met...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Added a display_progress keyword argument for MCMC classes which can be used to suppress progress me...
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and pred...
This release contains significant improvements to the GpRegressor class, including: A new option to...
Fixed a bug in the ChangePoint covariance kernel which was causing GpRegressor to incorrectly assess...
Rather than assuming the mean of the Gaussian process is zero, GpRegressor now treats the mean as a ...
Added a WhiteNoise covariance function to model the presence of Gaussian noise on input data for Gau...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
Added a new method GpRegressor.gradient, which allows for the calculation of the mean and variance o...
GpRegressor now supports multi-start gradient-based hyper-parameter optimisation using the L-BFGS-B ...
Added the HeteroscedasticNoise covariance kernel to the inference.covariance module, which allows fo...
Three new modules have been added to support the construction of likelihood, prior and posterior dis...
Improved numerical efficiency of the leapfrog update in HamiltonianChain. Fixed some errors so that ...
Fixed a bug introduced in the 0.5.0 release where a passing single spatial point to the __call__ met...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Added a display_progress keyword argument for MCMC classes which can be used to suppress progress me...
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and pred...
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