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Found 7 papers, 1 papers with code
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic programs in these modeling languages given observed data.
Using probabilistic programs as proposals
Monte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals.
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
This paper introduces the auxiliary inference divergence estimator (AIDE), an algorithm for measuring the accuracy of approximate inference algorithms.
Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion.
Encapsulating models and approximate inference programs in probabilistic modules
This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inference system.
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error.
Quantifying the probable approximation error of probabilistic inference programs
This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Monte Carlo approaches.
