14 research outputs found

    Tea: A High-level Language and Runtime System for Automating Statistical Analysis

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    Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.Comment: 11 page

    Probabilistic Programming with Densities in SlicStan: Efficient, Flexible, and Deterministic

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    Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantics of the language has been mainly given in terms of intuition about implementation, and has not been formalised. This paper provides a formal treatment of the Stan language, and introduces the probabilistic programming language SlicStan --- a compositional, self-optimising version of Stan. Our main contributions are: (1) the formalisation of a core subset of Stan through an operational density-based semantics; (2) the design and semantics of the Stan-like language SlicStan, which facilities better code reuse and abstraction through its compositional syntax, more flexible functions, and information-flow type system; and (3) a formal, semantic-preserving procedure for translating SlicStan to Stan

    OASIcs, Volume 67, PLATEAU\u2718, Complete Volume

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    OASIcs, Volume 67, PLATEAU\u2718, Complete Volum

    Using SyGuS to Synthesize Reactive Motion Plans

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    We present an approach for synthesizing reactive robot motion plans, based on compilation to Syntax-Guided Synthesis (SyGuS) specifications. Our method reduces the motion planning problem to the problem of synthesizing a function that can choose the next robot action in response to the current state of the system. This technique offers reactivity not by generating new motion plans throughout deployment, but by synthesizing a single program that causes the robot to reach its target from any system state that is consistent with the system model. This approach allows our tool to handle environments with adversarial obstacles. This work represents the first use of the SyGuS formalism to solve robot motion planning problems. We investigate whether using SyGuS for a bounded two-player reachability game is practical at this point in time

    Front Matter, Table of Contents, Preface, Conference Organization

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    Front Matter, Table of Contents, Preface, Conference Organizatio

    Front Matter, Table of Contents, Preface, Conference Organization

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    Front Matter, Table of Contents, Preface, Conference Organizatio

    OASIcs, Vol. 76, PLATEAU 2019, Complete Volume

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    OASIcs, Vol. 76, PLATEAU 2019, Complete Volum
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