  We formulate natural gradient variational inference (VI), expectation
propagation (EP), and posterior linearisation (PL) as extensions of Newton's
method for optimising the parameters of a Bayesian posterior distribution. This
viewpoint explicitly casts inference algorithms under the framework of
numerical optimisation. We show that common approximations to Newton's method
from the optimisation literature, namely Gauss-Newton and quasi-Newton methods
(e.g., the BFGS algorithm), are still valid under this 'Bayes-Newton'
framework. This leads to a suite of novel algorithms which are guaranteed to
result in positive semi-definite (PSD) covariance matrices, unlike standard VI
and EP. Our unifying viewpoint provides new insights into the connections
between various inference schemes. All the presented methods apply to any model
with a Gaussian prior and non-conjugate likelihood, which we demonstrate with
(sparse) Gaussian processes and state space models.
