Higher-Order Operator Precedence Languages

被引:2
作者
Reghizzi, Stefano Crespi [1 ,2 ]
Pradella, Matteo [1 ,2 ]
机构
[1] Politecn Milan, DEIB, Via Ponzio 34-5, I-20134 Milan, Italy
[2] CNR, IEIIT, Via Ponzio 34-5, I-20134 Milan, Italy
来源
ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE | 2017年 / 252期
关键词
INFERENCE;
D O I
10.4204/EPTCS.252.11
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Floyd's Operator Precedence (OP) languages are a deterministic context-free family having many desirable properties. They are locally and parallely parsable, and languages having a compatible structure are closed under Boolean operations, concatenation and star; they properly include the family of Visibly Pushdown (or Input Driven) languages. OP languages are based on three relations between any two consecutive terminal symbols, which assign syntax structure to words. We extend such relations to k-tuples of consecutive terminal symbols, by using the model of strictly locally testable regular languages of order k >= 3. The new corresponding class of Higher-order Operator Precedence languages (HOP) properly includes the OP languages, and it is still included in the deterministic (also in reverse) context free family. We prove Boolean closure for each subfamily of structurally compatible HOP languages. In each subfamily, the top language is called max-language. We show that such languages are defined by a simple cancellation rule and we prove several properties, in particular that max-languages make an infinite hierarchy ordered by parameter k. HOP languages are a candidate for replacing OP languages in the various applications where they have have been successful though sometimes too restrictive.
引用
收藏
页码:86 / 100
页数:15
相关论文
共 50 条
[31]   Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models [J].
Schultheiss, Christoph ;
Buhlmann, Peter ;
Yuan, Ming .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2024, 119 (546) :1019-1031
[32]   Adaptive Thouless-Anderson-Palmer Equation for Higher-order Markov Random Fields [J].
Takahashi, Chako ;
Yasuda, Muneki ;
Tanaka, Kazuyuki .
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 2020, 89 (06)
[33]   Speed and accuracy improvement of higher-order epistasis detection on CUDA-enabled GPUs [J].
Juenger, Daniel ;
Hundt, Christian ;
Gonzalez Dominguez, Jorge ;
Schmidt, Bertil .
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2017, 20 (03) :1899-1908
[34]   Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach [J].
Han, Xiaoyi ;
Hsieh, Chih-Sheng ;
Lee, Lung-fei .
REGIONAL SCIENCE AND URBAN ECONOMICS, 2017, 63 :97-120
[35]   Emergence of Higher-Order Transitivity across Development: The Importance of Local Task Difficulty. [J].
Kloos, Heidi .
2011 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING (ICDL), 2011,
[36]   Higher-Order Asymptotics and Its Application to Testing the Equality of the Examinee Ability Over Two Sets of Items [J].
Sinharay, Sandip ;
Jensen, Jens Ledet .
PSYCHOMETRIKA, 2019, 84 (02) :484-510
[37]   UTILITY OF A LARGE, MULTIGENE PLASTID DATA SET IN INFERRING HIGHER-ORDER RELATIONSHIPS IN FERNS AND RELATIVES (MONILOPHYTES) [J].
Rai, Hardeep S. ;
Graham, Sean W. .
AMERICAN JOURNAL OF BOTANY, 2010, 97 (09) :1444-1456
[38]   Multivariate copula-based conditional quantiles: analytic higher-order moments and ratio estimation approaches [J].
Hakim, Arief ;
Salman, A. N. M. ;
Syuhada, Khreshna .
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2024, 94 (18) :4128-4178
[39]   INFERRING THE HIGHER-ORDER PHYLOGENY OF MOSSES (BRYOPHYTA) AND RELATIVES USING A LARGE, MULTIGENE PLASTID DATA SET [J].
Chang, Ying ;
Graham, Sean W. .
AMERICAN JOURNAL OF BOTANY, 2011, 98 (05) :839-849
[40]   HIGHER-ORDER ACCURATE, POSITIVE SEMIDEFINITE ESTIMATION OF LARGE-SAMPLE COVARIANCE AND SPECTRAL DENSITY MATRICES [J].
Politis, Dimitris N. .
ECONOMETRIC THEORY, 2011, 27 (04) :703-744