Added new set of Jupyter notebook demos, which can be found in the /demos/ directory Added a new function inference.plotting.hdi_plot for convenient plotting of highest-density intervals derived from a sample of model realisations. All sampling classes in inference.mcmc now pass model parameters to the user-provided posterior function as a numpy.ndarray, and the documentation has been updated to reflect this
Added feature to results structure to store all parameters names, regardless of whether or not they ...
This release contains significant improvements to the GpRegressor class, including: A new option to...
BayesicFitting is a PYTHON toolbox for (Bayesian) data modelling and evidence calculation. It consis...
Three new modules have been added to support the construction of likelihood, prior and posterior dis...
Added a display_progress keyword argument for MCMC classes which can be used to suppress progress me...
Added a new module inference.approx for approximate inference tools. Currently contains the get_cond...
Fixed various bugs that appeared when testing after updating dependencies to numpy 1.15.0, scipy 1.3...
Highlights loo-pit plot implemented html_repr of InferenceData objects for jupyter notebooks Suppor...
Improved numerical efficiency of the leapfrog update in HamiltonianChain. Fixed some errors so that ...
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Added a new class GpLinearInverter for performing Gaussian-process linear inversion. Added a new cov...
Release v1.1.0 includes changes suggested by the reviewers for publication by the Journal of Open So...
I have introduced a class for doing the classification and making plots which makes the inference ti...
This major release of hIPPYlib includes the following new features: Non-Gaussian Bayesian inference...
This repository includes all the scripts used for sampling in the manuscript entitled "Posterior mar...
Added feature to results structure to store all parameters names, regardless of whether or not they ...
This release contains significant improvements to the GpRegressor class, including: A new option to...
BayesicFitting is a PYTHON toolbox for (Bayesian) data modelling and evidence calculation. It consis...
Three new modules have been added to support the construction of likelihood, prior and posterior dis...
Added a display_progress keyword argument for MCMC classes which can be used to suppress progress me...
Added a new module inference.approx for approximate inference tools. Currently contains the get_cond...
Fixed various bugs that appeared when testing after updating dependencies to numpy 1.15.0, scipy 1.3...
Highlights loo-pit plot implemented html_repr of InferenceData objects for jupyter notebooks Suppor...
Improved numerical efficiency of the leapfrog update in HamiltonianChain. Fixed some errors so that ...
Added a new MCMC sampling class EnsembleSampler, which is an implementation of the 'affine-invariant...
Added a new class GpLinearInverter for performing Gaussian-process linear inversion. Added a new cov...
Release v1.1.0 includes changes suggested by the reviewers for publication by the Journal of Open So...
I have introduced a class for doing the classification and making plots which makes the inference ti...
This major release of hIPPYlib includes the following new features: Non-Gaussian Bayesian inference...
This repository includes all the scripts used for sampling in the manuscript entitled "Posterior mar...
Added feature to results structure to store all parameters names, regardless of whether or not they ...
This release contains significant improvements to the GpRegressor class, including: A new option to...
BayesicFitting is a PYTHON toolbox for (Bayesian) data modelling and evidence calculation. It consis...
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