Simultaneous Inference on Multiple Assertions

2015 ◽  
pp. 201-220
Keyword(s):  
Simultaneous Inference

scholarly journals Simultaneous inference for time-varying models

2021 ◽  
Author(s):  
Sayar Karmakar ◽  
Stefan Richter ◽  
Wei Biao Wu
Keyword(s):  
Simultaneous Inference ◽  
Time Varying ◽  
Time Varying Models

Simultaneous inference of the compressibility and inelastic response of tantalum under extreme loading

2021 ◽  
Vol 130 (5) ◽  
pp. 055901
Author(s):  
W. J. Schill ◽  
R. A. Austin ◽  
K. L. Schimdt ◽  
J. L. Brown ◽  
N. R. Barton
Keyword(s):  
Simultaneous Inference ◽  
Inelastic Response

Simultaneous inference for functional data in sports biomechanics

2021 ◽  
Author(s):  
Todd Colin Pataky ◽  
Konrad Abramowicz ◽  
Dominik Liebl ◽  
Alessia Pini ◽  
Sara Sjöstedt de Luna ◽  
...  
Keyword(s):  
Functional Data ◽  
Simultaneous Inference ◽  
Sports Biomechanics

scholarly journals Compositional learning of mutually recursive procedural systems

2021 ◽  
Author(s):  
Markus Frohme ◽  
Bernhard Steffen
Keyword(s):  
Finite Automata ◽  
Simultaneous Inference ◽  
Recursive Structure ◽  
Recursive Models ◽  
Membership Queries ◽  
Compositional Approach ◽  
Recursive Programs ◽  
Automata Learning ◽  
The Individual ◽  
Compositional Learning

AbstractThis paper presents a compositional approach to active automata learning of Systems of Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to systems of DFAs that can mutually call each other. SPAs are of high practical relevance, as they allow one to efficiently learn intuitive recursive models of recursive programs after an easy instrumentation that makes calls and returns observable. Key to our approach is the simultaneous inference of individual DFAs for each of the involved procedures via expansion and projection: membership queries for the individual DFAs are expanded to membership queries of the entire SPA, and global counterexample traces are transformed into counterexamples for the DFAs of concerned procedures. This reduces the inference of SPAs to a simultaneous inference of the DFAs for the involved procedures for which we can utilize various existing regular learning algorithms. The inferred models are easy to understand and allow for an intuitive display of the procedural system under learning that reveals its recursive structure. We implemented the algorithm within the LearnLib framework in order to provide a ready-to-use tool for practical application which is publicly available on GitHub for experimentation.

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