Wang and Fleet's 2008 paper, "Gaussian Process Dynamical Models for Human Motion", introduces a Gaussian process latent variable model with Gaussian process latent dynamics. I discuss this paper in detail.

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A Gaussian process latent variable model (GPLVM) can be viewed as a generalization of probabilistic principal component analysis (PCA) in which the latent maps are Gaussian-process distributed. I discuss this relationship.

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Linderman, Johnson, and Adam's 2015 paper, "Dependent multinomial models made easy: Stick-breaking with the Pólya-gamma augmentation", introduces a Gibbs sampler for Gaussian processes with multinomial observations. I discuss this model in detail.

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The posterior mean from a Gaussian process regressor is related to the prediction of a kernel ridge regressor. I explore this connection in detail.

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I discuss Rasmussen and Williams's Algorithm 2.1 for an efficient implementation of Gaussian process regression.

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The definition of a Gaussian process is fairly abstract: it is an infinite collection of random variables, any finite number of which are jointly Gaussian. I work through this definition with an example and provide several complete code snippets.

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