Probabilistic Programming in Anglican

TL;DR: This work adapts the classic operational semantics of λ-calculus to a continuous setting via creating a measure space on terms and defining step-indexed approximations, and proves equivalence of big-step and small-step formulations of this distribution-based semantics.

TL;DR: Inference metaprogramming enables the concise expression of probabilistic models and inference algorithms across diverse elds, such as computer vision, data science, and robotics, within a single Probabilistic programming language.

TL;DR: The aim of this paper is to propose a novel approach toference called Automated Variational Inference for Probabilistic Programming, which allows programmers to specify a stochastic process using syntax that resembles modern programming lan 2.

TL;DR: The particle cascade is an asynchronous, anytime alternative to traditional sequential Monte Carlo algorithms that is amenable to parallel and distributed implementations and provides an unbiased marginal likelihood estimator which can be straightforwardly plugged into existing pseudo-marginal methods.

TL;DR: This work presents a technique that recovers hand-coded levels of performance from a universal probabilistic language, for the Metropolis-Hastings (MH) MCMC inference algorithm, that takes a Church program as input and traces its execution to remove computation overhead.

TL;DR: In this paper, a formalism to adapt ancestor resampling, a technique that mitigates particle degeneracy, to the probabilistic programming setting is presented, and empirical results demonstrate nontrivial performance gains.

TL;DR: This work introduces Church, a universal language for describing stochastic generative processes, based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset.