DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

2018/07/12 by Andrew R. Lawrence, Lawrence, Andrew R., Carl Henrik Ek +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1807.04833

Abstract

We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of the Dirichlet process as a specific structural prior allows our model to circumvent issues associated with previous Gaussian process latent variable models. Inference is performed by deriving an efficient variational bound on the marginal log-likelihood on the model.

Citations

• "General Intelligence," Objectively Determined and Measured

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