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RELEASE-NOTES.md

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This version includes two major contributions from our Google Summer of Code 2017 students:
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- Maxim Kucherov extended and refactored the variational inference module. This primarily adds two important classes, representing operator variational inference (`OPVI`) objects and `Approximation` objects. These make it easier to extend existing `variational` classes, and to derive inference from `variational` optimizations, respectively. The `variational` module now also includes normalizing flows (`NFVI`).
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- William Engels added an extensive new Gaussian processes (`gp`) module. Standard GPs can be specified using either `Latent` or `Marginal` classes, depending on the nature of the underlying function. A Student-T process `TP` has been added. In order to accomodate larger datasets, approximate marginal Gaussian processes (`MarginalSparse`) have been added.
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- Bill Engels added an extensive new Gaussian processes (`gp`) module. Standard GPs can be specified using either `Latent` or `Marginal` classes, depending on the nature of the underlying function. A Student-T process `TP` has been added. In order to accomodate larger datasets, approximate marginal Gaussian processes (`MarginalSparse`) have been added.
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Documentation has been improved as the result of the project's monthly "docathons".
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