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Deriving Probability Density Functions from Probabilistic Functional Programs

Sooraj Bhat ; Johannes Borgström ; Andrew D. Gordon ; Claudio Russo.

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently&nbsp;[&hellip;]

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Measure Transformer Semantics for Bayesian Machine Learning

Johannes Borgström ; Andrew D Gordon ; Michael Greenberg ; James Margetson ; Jurgen Van Gael.

The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing&nbsp;[&hellip;]

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