Monthly Archives: January 2018

Probabilistic Programming for Robotics

Autonomous agents use noisy sensors and actuators to interact with a complex external world. As such, inference engines and point estimators are essential to making modern robots work. Often, these are highly specialized and optimized to run in real-time on …

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Probabilistic Program Equivalence for NetKAT

Over the past decade, formal methods have found successful application in the verification of computer networks and various network verification tools have been developed in academia and industry. These tools are all based on deterministic network models, and as such …

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Contextual Equivalence for a Probabilistic Language with Continuous Random Variables and Recursion

We present a complete reasoning principle for contextual equivalence in an untyped probabilistic programming language. The language includes continuous random variables, conditionals, and scoring. The language also includes recursion, since in an untyped language the standard call-by-value fixpoint combinator is …

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Comparing the speed of probabilistic processes

A key aspect of many probabilistic systems such as continuous-time Markov chains is that of time: time passes as the system moves from state to state, and many properties of interest, such as safety properties, are concerned with time, e.g. …

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S-finite Kernels and Game Semantics for Probabilistic Programming

Staton has recently argued convincingly that first-order functional probabilistic programs with sampling from continuous distributions and soft constraints correspond precisely to so-called s-finite kernels. This class of possibly infinite kernels is a little studied extension of their better known σ-finite …

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The Support Method for Computing Expectations

In this abstract, we discuss an alternative method to sampling for computing the answer to probabilistic expectation queries: enumerating values in the support of the model and adding their contribution to the expectation. We propose several criteria for a good …

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The semantic structure of quasi-Borel spaces

Higher-order functions — functions that take functions as arguments — are incredibly useful as a programming abstraction. But what does a higher-order probabilistic program actually mean? We are used to thinking of program functions as mathematical functions, but does this …

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