Probabilistic-style programming (specify the model, not the
algorithm).
Non-Gaussian likelihoods.
Multivariate outputs.
Dimensionality reduction.
Approximations for large data sets.
Non Gaussian Likelihoods
[Gaussian processes model functions. If our observation is a
corrupted version of this function and the corruption process is
also Gaussian, it is trivial to account for this. However,
there are many circumstances where our observation may be non Gaussian.
In these cases we need to turn to approximate inference techniques. As a
simple illustration, we’ll use a dataset of binary observations of the
language that is spoken in different regions of East-Timor. First we
will load the data and a couple of libraries to visualize it.}
Olympic Marathon Data
Gold medal times for Olympic Marathon since 1896.
Marathons before 1924 didn’t have a standardized distance.
Present results using pace per km.
In 1904 Marathon was badly organized leading to very slow
times.
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