Inference-tools

- Added a new MCMC sampling class `EnsembleSampler`, which is an implementation of the 'affine-invariant ensemble sampler'.

- Improved the efficiency of linear algebra calculations in `GpRegressor` related to hyper-parameter optimisation.
- Initial guess generation for acquisition function maximisation in `GpOptimiser` now works properly in cases where some of the data are outside the search bounds.
- The choice of optimiser used for acquisition function maximisation in `GpOptimiser` can now also be specified when calling `GpOptimiser.propose_evaluation`.

- Added a `WhiteNoise` covariance function to model the presence of Gaussian noise on input data for Gaussian-process regression.
- 'Composite' covariance functions can now be constructed via addition - see the updated [GP regression notebook demo](https://github.com/C-bowman/inference-tools/blob/master/demos/gp_regression_demo.ipynb).
- The `gp_tools` and `pdf_tools` modules have been renamed to `gp` and `pdf` respectively. This is not a breaking change - importing using the old module names is still supported, but now raises a warning.

Fixed an import in `inference.__init__` which was causing issues for python 3.6 and 3.7 in some cases.

- Coverage and specificity of tests has been improved.
- Testing is now automated through GitHub actions.
- Code formatting is now automatically standardised using [Black](https://github.com/psf/black).
- Code for the package documentation has been moved into the repository (was previously kept in a separate repo).

Three new modules have been added to support the construction of likelihood, prior and posterior distributions.

- The `inference.likelihoods` module provides classes for constructing likelihood functions for a given user-defined forward-model. Currently the module includes classes for constructing Gaussian, Cauchy and logistic likelihoods. Classes for additional distributions are planned for inclusion in a future release.

- The `inference.priors` module provides classes for constructing a prior distributions over model variables. Currently the module includes classes for constructing Gaussian, uniform and exponential priors. Classes for additional distributions are planned for inclusion in a future release.

- The `Posterior` class from the `inference.posterior` module provides a convenient means of combining a likelihood and prior distribution function into a single posterior distribution function.

The newly added [Gaussian fitting jupyter notebook demo](https://github.com/C-bowman/inference-tools/blob/master/demos/gaussian_fitting_demo.ipynb) includes examples of how classes from these new modules can be used.