Improving Statistical Inference through Flexible Approximations

Keywords

Inference, Statistical inference, Computer science, Machine learning, Flexibility (engineering)

References

• Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable ModelsNeil D. Lawrence — 2005
• The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloMatthew D. Homan, Andrew Gelman — 2014
• A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov modelsJeffrey A. Bilmes — 1998
• Convergence rates of posterior distributions for non-i.i.d. observationsGhosal, S., Vaart, A.W. van der — 2007
• Flexible Bayesian Dynamic Modeling of Covariance and Correlation MatricesShiwei Lan, Andrew J. Holbrook, Norbert J. Fortin — 2017
• Stochastic Gradient Descent as Approximate Bayesian InferenceStephan Mandt, Matthew D. Hoffman, David M. Blei — 2017
• Autoencoders, Unsupervised Learning, and Deep ArchitecturesPierre Baldi — 2011
• Slice SamplingRadford M. Neal — 2000

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