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Towards Precision of Probabilistic Bounds Propagation [article]

Helmut Thone, Ulrich Guntzer, Werner Kiessling
2013 arXiv   pre-print
In particular, we provide new precise analytical bounds for probabilistic entailment.  ...  The basic inference mechanism relies on local bounds propagation, implementable by deductive databases with a bottom-up fixpoint evaluation.  ...  approach at the user in terface as INFERNO and [AnHo 90 Towards Precision of Probabilistic Bounds Propagation 317 To contrast this sort of local deduction with the global operations research optimization  ... 
arXiv:1303.5434v1 fatcat:mfudc2qeyveahiwju4dnt3qpl4

An automated, efficient and static bit-width optimization methodology towards maximum bit-width-to-error tradeoff with affine arithmetic model

Yu Pu, Yajun Ha
2006 Proceedings of the 2006 conference on Asia South Pacific design automation - ASP-DAC '06  
Chen, "Towards efficient static analysis of finite precision effects in DSP applications via affine arithmetic modeling," in Proceedings of 40th Design Automation Conference, pp.496 -501, 2003. [2] C.  ...  Outline Probabilistic error analysis In most of the DSP designs, the designer allows certain degree of error rate, which enables a larger bit-width-to-error tradeoff than using hard error analysis.  ...  We use Gaussian approximation during analysis, theoretically it is difficult to fully guarantee the error probability to be bounded.  ... 
doi:10.1145/1118299.1118500 fatcat:tid5bumj2fh4bjewyhrnc5t2yy

Fast, accurate static analysis for fixed-point finite-precision effects in DSP designs

C.F. Fang, R.A. Rutenbar, Tsuhan Chen
2003 ICCAD-2003. International Conference on Computer Aided Design (IEEE Cat. No.03CH37486)  
The technique is based on recent interval representation methods from affine arithmetic, and the use of new probabilistic bounds.  ...  These so-called "lightweight float" formats 171 can mitigate some of the pain of translating from full to limited precision.  ...  Towards efficient static analyis of fi nile precision effects in DSP applications via affine arithmetic modeling. In Design Aummarion Conference, PUrpoSt? Verification Data lype: 6.  ... 
doi:10.1109/iccad.2003.159701 fatcat:l52zbbvbm5fghfccnugwbbnc2m

Anytime Exact Belief Propagation [article]

Gabriel Azevedo Ferreira, Quentin Bertrand, Charles Maussion, Rodrigo de Salvo Braz
2017 arXiv   pre-print
Statistical Relational Models and, more recently, Probabilistic Programming, have been making strides towards an integration of logic and probabilistic reasoning.  ...  We believe that, among the probabilistic reasoning algorithms, Belief Propagation is the most similar to logic reasoning: messages are propagated among neighboring variables, and the paths of message-passing  ...  Introduction Statistical Relational Models (Getoor and Taskar 2007) and, more recently, Probabilistic Programming, have been making strides towards probabilistic logic inference algorithms that integrate  ... 
arXiv:1707.08704v1 fatcat:bsj66st2ojczzmftpxjbuzccki

Increased robustness of Bayesian networks through probability intervals

Helmut Thöne, Ulrich Güntzer, Werner Kieβling
1997 International Journal of Approximate Reasoning  
A set of local inference rules is developed, which is proved to be sound and--in the absence of loops--also to be complete; i.e., tightest probability bounds can be computed incrementally by bounds propagation  ...  Such investigations can be employed for improving network design towards more robust and reliable decision analysis. © 1997 Elsevier Science Inc.  ...  Thus in the light of opposing evidences some bounds tend to diffuse toward the unit interval because of an increased sensitivity.  ... 
doi:10.1016/s0888-613x(96)00138-7 fatcat:nvksh3zdizd6fcyh7haov7a6iy

Probabilistic Attention for Interactive Segmentation [article]

Prasad Gabbur and Manjot Bilkhu and Javier Movellan
2021 arXiv   pre-print
A PyTorch layer implementation of our probabilistic attention model will be made publicly available here: https://github.com/apple/ml-probabilistic-attention.  ...  We provide a probabilistic interpretation of attention and show that the standard dot-product attention in transformers is a special case of Maximum A Posteriori (MAP) inference.  ...  These values are propagated globally through the attention mechanism to directly and more effectively influence the outputs towards user intended values.  ... 
arXiv:2106.15338v2 fatcat:465ohrw465cldfrb4qtxgax35u

A Probabilistic Approach to Floating-Point Arithmetic [article]

Fredrik Dahlqvist and Rocco Salvia and George A Constantinides
2019 arXiv   pre-print
Finite-precision floating point arithmetic unavoidably introduces rounding errors which are traditionally bounded using a worst-case analysis.  ...  computation and propagate it through each program instruction.  ...  On each sub-interval they combine probabilistic affine arithmetic, to propagate the error terms through the AST of the program, together with worst-case static analysis to bound the imprecision error term  ... 
arXiv:1912.00867v1 fatcat:te5k7dnorfeftfa64gqboyhnzq

Modeling Enlargement Attacks Against UWB Distance Bounding Protocols

Alberto Compagno, Mauro Conti, Antonio Alberto D'Amico, Gianluca Dini, Pericle Perazzo, Lorenzo Taponecco
2016 IEEE Transactions on Information Forensics and Security  
The contribution of this paper is to provide a probabilistic model for the success of an enlargement attack against a distance bounding protocol realized with the IEEE 802.15.4a UWB standard.  ...  The model captures several variables, like the propagation environment, the signal-to-noise ratio, and the time-of-arrival (TOA) estimation algorithm.  ...  CONCLUSIONS In this paper, we provided a probabilistic model of the outcome of an overshadowing attack against a distance bounding protocol realized with IEEE 802.15.4a UWB.  ... 
doi:10.1109/tifs.2016.2541613 fatcat:etpy7m4mobd7vh5rjdscasiif4

Numerical accuracy and efficiency in the propagation of epistemic and aleatory uncertainties

Eric Chojnacki, Jean Baccou, Sébastien Destercke
2010 International Journal of General Systems  
The result of this propagation is a random fuzzy variable. When dealing with complex models, the computational cost of such a propagation quickly becomes too high.  ...  One way to do so is to model aleatory uncertainty by classical probability distributions and epistemic uncertainty by means of possibility distributions, and then propagate them by their respective calculus  ...  Since for γ E = {α = 0} and the probabilistic approach, intervals reduce to single values, we have six series of 1000 values (corresponding to lower/upper bounds of γ E = {α = 1, 0, av} and to the probabilistic  ... 
doi:10.1080/03081079.2010.500796 fatcat:vglxqyo6ebfh5di3st3gq3kh5u

Abstraction refinement guided by a learnt probabilistic model

Radu Grigore, Hongseok Yang
2016 Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages - POPL 2016  
For each untried abstraction, our probabilistic model provides a probability of success, while the size of the abstraction provides an estimate of its cost in terms of analysis time.  ...  Our probabilistic model is a variant of the Erdős-Rényi random graph model, and it is tunable by what we call hyperparameters.  ...  We thank Mayur Naik for giving us access to the private parts of Chord [39] . We thank Yongsu Park for giving us access to a server on which we ran preliminary experiments.  ... 
doi:10.1145/2837614.2837663 dblp:conf/popl/GrigoreY16 fatcat:kh47qwjttjagpjksp2tkk2opum

A Study of Approximate Data Management Techniques for Sensor Networks

Adonis Skordylis
2006 2006 International Workshop on Intelligent Solutions in Embedded Systems  
A large variety of applications could benefit from the pervasive deployment of inexpensive wireless sensor nodes, ranging from environmental monitoring to emergency detection and response.  ...  Recent developments in sensor network technology have enabled the instrumentation of the physical world with smart devices for monitoring purposes.  ...  Adaptive distribution of error bounds: The experts of an application domain define carefully the degree of precision (also referred to as error bound) that they are willing to tolerate for their queries  ... 
doi:10.1109/wises.2006.237157 fatcat:ggs3mikolneerev7et4gextwcu

Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments [article]

Weiqiao Han and Ashkan Jasour and Brian Williams
2022 arXiv   pre-print
We address the risk bounded trajectory optimization problem of stochastic nonlinear robotic systems.  ...  The goal is to plan a sequence of control inputs for the robot to navigate to the target while bounding the probability of colliding with obstacles.  ...  More precisely, the risk bounded trajectory optimization problem can be formulated as the following probabilistic optimization: Problem 1.  ... 
arXiv:2203.03038v1 fatcat:zj5hykklmjainlwtq4x5trheee

A Study of Approximate Data Management Techniques for Sensor Networks

Adonis Skordylis, Niki Trigoni, Alexandre Guitton
2006 2006 International Workshop on Intelligent Solutions in Embedded Systems  
A large variety of applications could benefit from the pervasive deployment of inexpensive wireless sensor nodes, ranging from environmental monitoring to emergency detection and response.  ...  Recent developments in sensor network technology have enabled the instrumentation of the physical world with smart devices for monitoring purposes.  ...  Adaptive distribution of error bounds: The experts of an application domain define carefully the degree of precision (also referred to as error bound) that they are willing to tolerate for their queries  ... 
doi:10.1109/wises.2006.329119 dblp:conf/wises/SkordylisTG06 fatcat:ay3klpfembarrguz7oi2ktdysq

When is it worthwhile to propagate a constraint? A probabilistic analysis of AllDifferent

Jérémie du Boisberranger, Danièle Gardy, Xavier Lorca, Charlotte Truchet
2013 2013 Proceedings of the Tenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO)  
We propose to quantify this phenomenon in the particular case of the AllDifferent constraint (bound consistency propagator).  ...  This article presents new work on analyzing the behaviour of a constraint solver, with a view towards optimization.  ...  Given a set of variable domains, we provide a probabilistic indicator that allows us to predict if the AllDifferent propagator, for bound-consistency, will detect and remove some inconsistent part of these  ... 
doi:10.1137/1.9781611973037.10 dblp:conf/analco/BoisberrangerGLT13 fatcat:2ucwa2kzknfu7alodnmtxk2ipy

Line sampling and Fuzzy Interval Analysis for the propagation of aleatory and epistemic uncertainties in risk models [chapter]

E Ferrario, N Pedroni, E Zio, E Zio
2013 Safety, Reliability and Risk Analysis  
of hybrid (probabilistic and possibilistic) uncertainties through a model for the riskbased design of a flood protection dike.  ...  paper, an advanced Monte Carlo (MC) simulation method, namely Line Sampling (LS), is considered in combination with Fuzzy Interval Analysis (FIA) for improving the sampling efficiency in the hierarchical propagation  ...  The propagation of the hybrid (probabilistic and possibilistic) level-2" hierarchical uncertainty repre- i.  ... 
doi:10.1201/b15938-498 fatcat:zxr5rkzd65c5xachf3ln7v3twa
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