Features

Support for variational and sparse models
Support for multi output (heterogeneous) likelihoods, i.e. different likelihoods for each channel
New models: Snelson, OpperArchambeau, Titsias, Hensman
New kernels: Constant, White, Exponential, LocallyPeriodic, Cosine, Sinc
New likelihoods: StudentT, Exponential, Laplace, Bernoulli, Beta, Gamma, Poisson, Weibull, LogLogistic, LogGaussian, ChiSquared
New mean functions: Constant and Linear
Allow kernels to be added and multiplied (i.e. K1 + K2 or K1 * K2)
Data and DataSet now accept more data types as input, such as pandas series
Data, DataSet, and Model plot functionalities return the figure and axes to allow customization
Support sampling (prior or posterior) from the model
Add the MOHSM kernel: multi-output harmonic spectral mixture kernel (Altamirano 2021)
Parameters can be pegged to other parameters, essentially removing them from training
Exact model supports training with known data point variances and draw their error bars in plots

Improvements

Jitter added to the diagonal before calculating the Cholesky is now relative to the average value of the diagonal, this improves numeric stability for all kernels irrespective of the actual numerical magnitude of the values
Kernels now implement K_diag that returns the kernel diagonal for better performance
BNSE initialization method has been reimplemented with improved performance and stability
Parameter initialization for all models from different initialization methods has been much improved
Induction point initialization now support random or grid or density
SpectralMixture (in addition to Spectral), MultiOutputSpectralMixture (in addition to MultiOutputSpectral) with higher performance
Allow mixing of single-output and multi-output kernels using active
All plotting functions have been restyled
Model training allows custom error function for calculation at each iteration
Support single and cross lengthscales for the SquaredExponential, RationalQuadratic, Periodic, LocallyPeriodic kernels
Add AIC and BIC methods to model
Add model.plot_correlation()

Remove rescale_x
Parameter.trainable => Parameter.train
Kernels are by default initialized deterministically and not random, however the models (MOSM, MOHSM, CONV, CSM, SM-LMC, and SM) are still initialized randomly by default
Plotting predictions happens from the model no the data: model.plot_prediction() instead of model.predict(); data.plot()