Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation
Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation explores A framework for scalable Bayesian inference using automatic differentiation to efficiently marginalize latent parameters in complex models.. Commercial viability score: 4/10 in Bayesian Inference.
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