Update News and deprecate GPMC

NEWS.md

@@ -4,6 +4,10 @@
 * Introduced sparse approximation methods
   * Subset of regressors, deterministic training conditional, fully independent training conditional and full-scale approximation are all available 
 * Extended functionality to include leave-one-out cross-validation
+* Introduced functionality to enable variational inference in GPs with non-Gaussian data.
+  * The approach used is a variant of that presented in [Khan et. al.](http://papers.nips.cc/paper/4535-fast-bayesian-inference-for-non-conjugate-gaussian-process-regression)
+  * Currently limited to only Poisson data. Additional likelihood functionality will be added soon.
+* Deprecated `GPMC` in favor of `GPA`. This is to be inline with that fact that approximate inference in a GP is not limited to MCMC, but variational methods can now be used.
 
 ## Version 0.9.0 (2018-12-07)
 * Introduction of `ElasticGPE` to allow `GP` which can grow without

src/GPA.jl

@@ -48,6 +48,7 @@ mutable struct GPA{X<:AbstractMatrix,Y<:AbstractVector{<:Real},M<:Mean,K<:Kernel
         initialise_target!(gp)
     end
 end
+@deprecate GPMC GPA
 
 function GPA(x::AbstractMatrix, y::AbstractVector{<:Real}, mean::Mean, kernel::Kernel, lik::Likelihood, covstrat::CovarianceStrategy)
     data = KernelData(kernel, x, x, covstrat)