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Metric Structures and Probabilistic Computation [article]

Wesley Calvert
2008 arXiv   pre-print
Later sections give examples of the application of this framework to various classes of structures, and to some problems of computational complexity theory.  ...  The present paper shows that probabilistic computation (sometimes called randomized computation) can play an analogous role for structures described in continuous first-order logic.  ...  Let B and C be isomorphic, probabilistically computable atomless probability structures with universesB andĈ, respectively.  ... 
arXiv:0806.0398v1 fatcat:ymupc3jdorhp5h7xplxcdkuive

Metric structures and probabilistic computation

Wesley Calvert
2011 Theoretical Computer Science  
We also show that probabilistically computable structures give rise to a model of ACA 0 in a natural way, and describe a connection with complexity theory.  ...  The present paper shows that probabilistic computation (sometimes called randomized computation) and continuous logic stand in a similar close relationship.  ...  Section 3 will define probabilistic Turing machines and the class of structures they compute. Section 4 will contain the proof of the main result.  ... 
doi:10.1016/j.tcs.2011.02.005 fatcat:7bydv5rc6jaejeyqfx5ln4kpai

Preface to special issue: ICTAC 2015

MARTIN LEUCKER, JORGE A. PÉREZ, CAMILO RUEDA, FRANK D. VALENCIA
2018 Mathematical Structures in Computer Science  
This issue of Mathematical Structures in Computer Science (MSCS) contains a selection of papers presented at the 12th International Colloquium on Theoretical Aspects of Computing (ICTAC 2015), which took  ...  They introduce a notion of probabilistic bisimulation and a metric variant.  ...  The paper by Bacci, Bacci, Larsen and Mardare (Converging from Branching to Linear Metrics on Markov Chains) considers metric analogues of probabilistic trace equivalence and probabilistic bisimilarity  ... 
doi:10.1017/s0960129518000130 fatcat:7e2hvlpvnjdkladbg6bzzfuzoa

MEDICAL IMAGES FUSION ALGORITHM BASED ON PROBABILISTIC GAMMA-NORMAL MODEL WITH STRUCTURE-TRANSFERRING PROPERTIES

I. A. Gracheva, A. V. Kopylov
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The proposed method gives the best result in terms of the spatial frequency metric and lower computation time than other image fusion methods.  ...  We propose here the new algorithm based on the probabilistic gamma-normal model with structure-transferring properties.  ...  The proposed method gives the best result in the terms of the spatial frequency metric and lower computation time than other image fusion methods.  ... 
doi:10.5194/isprs-archives-xliv-2-w1-2021-79-2021 fatcat:cmqhtb27qbcbfjwq2lk3fgup7y

Page 5774 of Mathematical Reviews Vol. , Issue 2000h [page]

2000 Mathematical Reviews  
(English summary) Applications of ordered sets in computer science (Braunschweig, 1996). Appl. Categ. Structures 7 (1999), no. 1-2, 85-111.  ...  We show that this category has partially-additive structure and, as such, supports basic constructs like iteration.  ... 

A review on probabilistic graphical models in evolutionary computation

Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana
2012 Journal of Heuristics  
Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these  ...  Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains.  ...  In univariate and bivariate EDAs, the probabilistic model structure is either fixed or is very restricted.  ... 
doi:10.1007/s10732-012-9208-4 fatcat:54ipbzsryfbt5nqmaczgurb2he

Probabilistic Proximity Searching Algorithms Based on Compact Partitions [chapter]

Benjamin Bustos, Gonzalo Navarro
2002 Lecture Notes in Computer Science  
In this paper, we push further in this direction by developing probabilistic algorithms on data structures whose exact versions are the best for high dimensions.  ...  The intrinsic dimension of a metric space is defined in [8] as µ 2 /2σ 2 , where µ and σ 2 are the mean and the variance of the distance histogram of the metric space.  ...  The first author is on leave from the Department of Computer Science, University of Chile.  ... 
doi:10.1007/3-540-45735-6_25 fatcat:dfxijnungrhj7g3cowpgwl5miq

Probabilistic proximity searching algorithms based on compact partitions

Benjamin Bustos, Gonzalo Navarro
2004 Journal of Discrete Algorithms  
In this paper, we push further in this direction by developing probabilistic algorithms on data structures whose exact versions are the best for high dimensions.  ...  The intrinsic dimension of a metric space is defined in [8] as µ 2 /2σ 2 , where µ and σ 2 are the mean and the variance of the distance histogram of the metric space.  ...  The first author is on leave from the Department of Computer Science, University of Chile.  ... 
doi:10.1016/s1570-8667(03)00067-4 fatcat:y66uonymwfg4zhgf5hhhmuwayu

Assertion Role in a Hybrid Link Prediction Approach through Probabilistic Ontology

Marcius Armada de Oliveira, Kate Revoredo, José Eduardo Ochoa Luna, Fábio Gagliardi Cozman
2013 Joint Seminar on Ontology Research in Brazil / International Workshop on Metamodels, Ontologies and Semantic Technologies  
In this paper, we encompass this problem by proposing an algorithm that combines structure and semantic metrics to find the set of relevant individuals.  ...  We empirically evaluate this proposal analyzing the assertion role of these individuals when predicting a link through a probabilistic ontology.  ...  In this paper, we argue that not only structural metrics can define the best set A(a, b) and we evaluate the performance of structural and semantic approaches for selecting the most relevant individuals  ... 
dblp:conf/ontobras/ArmadaRLC13 fatcat:anasnvvjkvdxdgjxp36vctl33y

Probabilistic language models in cognitive neuroscience: Promises and pitfalls

Kristijan Armeni, Roel M. Willems, Stefan L. Frank
2017 Neuroscience and Biobehavioral Reviews  
Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension.  ...  On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined.  ...  Acknowledgments The work presented here was partly funded by the Netherlands Organisation for Scientific Research (NWO) Gravitation Grant 024.001.006 to the Language in Interaction Consortium and Vidi  ... 
doi:10.1016/j.neubiorev.2017.09.001 pmid:28887227 fatcat:idc4sce5rveypgeq2wa33hiasi

Incorrect systems

Christoph M. Kirsch, Hannes Payer
2012 Proceedings of the 49th Annual Design Automation Conference on - DAC '12  
In particular, we discuss work on probabilistic and approximate design of processors, unreliable cores in asymmetric multi-core architectures, besteffort computing, stochastic processors, accuracy-aware  ...  program transformations, and relaxed concurrent data structures.  ...  computation as quality metric.  ... 
doi:10.1145/2228360.2228523 dblp:conf/dac/KirschP12 fatcat:rsc72bw4dfhqjhf7x5tdydk26a

Page 1493 of Mathematical Reviews Vol. 58, Issue 2 [page]

1979 Mathematical Reviews  
Furthermore, we demonstrate that ‘the class of all structurally stable continuous-time probabilistic automata is open, dense, convex and connected in the metric space of all continuous-time probabilistic  ...  Furthermore, we prove that ‘the class of structurally stable proba- bilistic automata is open, dense, convex and connected in the metric space of all probabilistic automata defined on the fixed State space  ... 

Uncertainty Estimation in Deep 2D Echocardiography Segmentation [article]

Lavsen Dahal, Aayush Kafle, Bishesh Khanal
2020 arXiv   pre-print
Recently, Deep Learning (DL) has been used in 2D echocardiography for automated view classification, and structure and function assessment.  ...  The performance of uncertainty models and quantification metric may depend on the prediction task and the models being compared.  ...  Quantifying Uncertainty We propose and compute a probabilistic atlas based uncertainty measure in addition to other three existing metrics -sample variance, predictive entropy and mutual information [  ... 
arXiv:2005.09349v1 fatcat:iqq4jl7aw5cjrfd45gfmlxh77i

A Survey of Bayesian Network Models for Decision Making System in Software Engineering

Nageswarao M., N. Geethanjali
2016 International Journal of Computer Applications  
Traditional Bayesian networks are system dependable and their models are invariant towards the accurate computation.  ...  A large number of software metrics are discovered and used for metric prediction in the literature.  ...  The training of input data needs large amount of data.indicates the prior probability of the metric type m and it is computed by dividing the number of metrics in type I by the total number of metrics  ... 
doi:10.5120/ijca2016906330 fatcat:cq45rwqsubaadfwszki26bap6q

Approximation Metrics Based on Probabilistic Bisimulations for General State-Space Markov Processes: A Survey

Alessandro Abate
2013 Electronical Notes in Theoretical Computer Science  
We deal with Markovian processes in discrete time evolving on general state spaces, namely on domains with infinite cardinality and endowed with proper measurability and metric structures.  ...  The focus of this work is to discuss approximation metrics between two such processes, based on the notion of probabilistic bisimulation: in particular we investigate metrics characterized by an approximate  ...  , on the notion of bisimulation for (deterministic and) probabilistic processes.  ... 
doi:10.1016/j.entcs.2013.12.002 fatcat:bda7mwwxzvarti2ok7wgrrkyt4
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