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We propose a quantum programming paradigm where all data are familiar classical data, and the only non-classical element is a random number generator that can return results with negative probability. Currently, the vast majority of quantum ...

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are ...

A key promise of probabilistic programming is the ability to specify rich models using an expressive program- ming language. However, the expressive power that makes probabilistic programming languages enticing also poses challenges to inference, so much ...

The development of a physical model begins with a knowledge model, initially existing as ideas in the mind of a researcher. A transition from knowledge models to strict mathematical formalisms is a challenging process, and may not always be feasible, ...

Universal probabilistic programming languages (PPLs) make it relatively easy to encode and automatically solve statistical inference problems. To solve inference problems, PPL implementations often apply Monte Carlo inference algorithms that rely ...

Probabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during inference and debugging are left entirely to the developers and typically require significant ...

We spell out the paradigm of exact conditioning as an intuitive and powerful way of conditioning on observations in probabilistic programs. This is contrasted with likelihood-based scoring known from languages such as Stan. We study exact conditioning in ...

We put forward an approach to the semantics of probabilistic programs centered on an action-based language equipped with a small-step operational semantics. This approach provides benefits in terms of both clarity and effective implementation. ...

Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming—graphical models include Bayesian networks and factor graphs. For modeling and formal verification of ...

We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, ...

• Process improvement practitioners display higher maturity scores.
• From reactive ...

Procurement maturity becomes a crucial indicator reflecting how effectively and efficiently a procurement function fulfills the expectations. Purchasing and supply management literature posits several maturity evaluation models ...

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov ...

Understanding the influence of configuration options on the performance of a software system is key for finding optimal system configurations, system understanding, and performance debugging. In the literature, a number of performance-influence ...

In probabilistic programming languages (PPLs), a critical step in optimization-based inference methods is constructing, for a given model program, a trainable guide program. Soundness and effectiveness of inference rely on constructing good guides, but ...

Developing denotational models for higher-order languages that combine probabilistic and nondeterministic choice is known to be very challenging. In this paper, we propose an alternative approach based on operational techniques. We study a higher-order ...

A broad range of natural and social systems from human microbiome to financial markets can go through critical transitions, where the system suddenly collapses to another stable configuration. Anticipating such transition early and accurately can ...

Stochastic approximation methods for variational inference have recently gained popularity in the probabilistic programming community since these methods are amenable to automation and allow online, scalable, and universal approximate ...

We present AQUA, a new probabilistic inference algorithm that operates on probabilistic programs with continuous posterior distributions. AQUA approximates programs via an efficient quantization of the continuous distributions. It represents the ...

This paper presents the first slicing approach for probabilistic programs based on specifications. We show that when probabilistic programs are accompanied by their specifications in the form of pre- and post-condition, we can exploit ...

• Probabilistic programs support slicing based on specification.
• The presence of ...

We use probabilistic program synthesis to solve questions in MIT and Harvard Probability and Statistics courses. Traditional approaches using the latest GPT-3 language model without program synthesis achieve a solve rate of 0.2 in these classes. ...