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Probabilistic programming languages (PPLs) provide language support for expressing flexible probabilistic models and solving Bayesian inference problems. PPLs with programmable inference make it possible for users to obtain improved results by ...

This article presents GenSQL, a probabilistic programming system for querying probabilistic generative models of database tables. By augmenting SQL with only a few key primitives for querying probabilistic models, GenSQL enables complex Bayesian ...

There are two approaches to automatically deriving symbolic worst-case resource bounds for programs: static analysis of the source code and data-driven analysis of cost measurements obtained by running the program. Static resource analysis is usually ...

This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian non-parametric prior over a symbolic space of Gaussian process time series models, we present a novel structure learning ...

How can we automate and scale up the processes of learning accurate probabilistic models of complex data and obtaining principled solutions to probabilistic inference and analysis queries? This thesis presents efficient techniques for addressing ...

We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries. SPPL translates probabilistic programs into sum-product ...

This paper addresses a fundamental problem in random variate generation: given access to a random source that emits a stream of independent fair bits, what is the most accurate and entropy-efficient algorithm for sampling from a discrete probability ...

Although probabilistic programming is widely used for some restricted classes of statistical models, existing systems lack the flexibility and efficiency needed for practical use with more challenging models arising in fields like computer vision and ...

We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad ...

Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed ...