Inference-tools

- Added a new module `inference.approx` for approximate inference tools.
- Currently contains the `get_conditionals` and `conditional_sample` functions for evaluating and sampling from conditional distributions.

- 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.

- Added the `HeteroscedasticNoise` covariance kernel to the `inference.covariance` module, which allows for the noise variance on each data-point to be varied independently.

- Fixed a bug where the `WhiteNoise` kernel would cause a crash when working with data having more than one dimension.

- Fixed a bug in the `ChangePoint` covariance kernel which was causing `GpRegressor` to incorrectly assess the number of covariance hyper-parameters, and subsequently crash during hyper-parameter optimisation.

- 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.