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Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.
A Universal Marginalizer for Amortized Inference in Generative Models
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances.
Modeling human function learning with Gaussian processes
Accounts of how people learn functional relationships between continuous variables have tended to focus on two possibilities: that people are estimating explicit functions, or that they are simply performing associative learning supported by similarity.
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