Main features and improvements
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- Added `SoftMax` likelihood (799)
- Added likelihoods where expectations are evaluated with Monte Carlo, `MonteCarloLikelihood` (799)
- GPflow monitor refactoring, check `monitor-tensorboard.ipynb` for details (792)
- Speedup testing on Travis using utility functions for configuration in notebooks (789)
- Support Python 3.5.2 in typing checks (Ubuntu 16.04 default python3) (787)
- Corrected scaling in Students-t likelihood variance (777)
- Removed jitter before taking the cholesky of the covariance in NatGrad optimizer (768)
- Added GPflow logger. Created option for setting logger level in `gpflowrc` (764)
- Improved quadrature for likelihoods. Unified quadrature method introduced - `ndiagquad` (736), (747)
- Added support for multi-output GPs, check `multioutput.ipynb` for details (724)
* Multi-output features
* Multi-output kernels
* Multi-dispatch for conditional
* Multi-dispatch for Kuu and Kuf
- Support Exponential distribution as prior (717)
- Added notebook to demonstrate advanced usage of GPflow, such as combining GP with Neural Network (712)
- Minibatch shape is `None` by default to allow dynamic change of data size (704)
- Epsilon parameter of the Robustmax likelihood is trainable now (635)
- GPflow model saver (660)
* Supports native GPflow models and provides an interface for defining custom savers for user's models
* Saver stores GPflow structures and pythonic types as numpy structured arrays and serializes them using HDF5
Bug fixes
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- Fixed bug at `params_as_tensors_for` (751)
- Fixed GPflow SciPy optimizer to pass options to _actual_ scipy optimizer correctly (738)