Random variables and their distributions are a central part in many areas of statistical
methods. The Distributions. jl package provides Julia users and developers tools for working …

A proposal for a formal model, Fragment Grammars, that treats productivity and reuse as the
target of inference in a probabilistic framework. Language allows us to express and …

Classical and Connectionist theories of cognitive architecture seek to explain systematicity
(ie, the property of human cognition whereby cognitive capacity comes in groups of related …

One of the appeals of pure functional programming is that it is so amenable to equational
reasoning. One of the problems of pure functional programming is that it rules out …

Two general techniques for implementing a domain-specific language (DSL) with less
overhead are the finally-tagless embedding of object programs and the direct-style …

The machine learning community has recently shown a lot of interest in practical
probabilistic programming systems that target the problem of Bayesian inference. Such …

The Bayesian approach to machine learning amounts to inferring posterior distributions of
random variables from a probabilistic model of how the variables are related (that is, a prior …

We present a Bayesian model of the mirror image problems of linguistic productivity and
reuse. The model, known as Fragment Grammar, is evaluated against several …

The goal of interactive machine learning is to help scientists and engineers exploit more
specialized data from within their deployed environment in less time, with greater accuracy …

We introduce AmiCo, a tool that extends a proof assistant, Isabelle/HOL, with flexible
function definitions well beyond primitive corecursion. All definitions are certified by the …