
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
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LFPPL: A LowLevel First Order Probabilistic Programming Language for NonDifferentiable Models
We develop a new Lowlevel, Firstorder Probabilistic Programming Langua...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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An Introduction to Probabilistic Programming
This document is designed to be a firstyear graduatelevel introduction...
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A Convenient Category for HigherOrder Probability Theory
Higherorder probabilistic programming languages allow programmers to wr...
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Spreadsheet Probabilistic Programming
Spreadsheet workbook contents are simple programs. Because of this, prob...
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Semantics for probabilistic programming: higherorder functions, continuous distributions, and soft constraints
We study the semantic foundation of expressive probabilistic programming...
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Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current stateof...
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On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators
We present a formalization of nested Monte Carlo (NMC) estimation, where...
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The BetaBernoulli process and algebraic effects
In this paper we analyze the BetaBernoulli process from Bayesian nonpar...
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Denotational validation of higherorder Bayesian inference
We present a modular semantic account of Bayesian inference algorithms f...
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Reparameterization Gradient for Nondifferentiable Models
We present a new algorithm for stochastic variational inference that tar...
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Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
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A Generalization of Hierarchical Exchangeability on Trees to Directed Acyclic Graphs
Motivated by problems in Bayesian nonparametrics and probabilistic progr...
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Towards Verified Stochastic Variational Inference for Probabilistic Programs
Probabilistic programming is the idea of writing models from statistics ...
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Stochastically Differentiable Probabilistic Programs
Probabilistic programs with mixed support (both continuous and discrete ...
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Differentiable Algorithm for Marginalising Changepoints
We present an algorithm for marginalising changepoints in timeseries mo...
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Hongseok Yang
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