## A General Framework for Probabilistic Characterizing Formulae

Publication: Research - peer-review › Conference article – Annual report year: 2012

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**A General Framework for Probabilistic Characterizing Formulae.** / Sack, Joshua; Zhang, Lijun.

Publication: Research - peer-review › Conference article – Annual report year: 2012

### Harvard

*Lecture Notes in Computer Science*, vol 7148, pp. 396-411., 10.1007/978-3-642-27940-9_26

### APA

*Lecture Notes in Computer Science*,

*7148*, 396-411. 10.1007/978-3-642-27940-9_26

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*Lecture Notes in Computer Science*. 2012, 7148. 396-411. Available: 10.1007/978-3-642-27940-9_26

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### RIS

TY - CONF

T1 - A General Framework for Probabilistic Characterizing Formulae

AU - Sack,Joshua

AU - Zhang,Lijun

PY - 2012

Y1 - 2012

N2 - Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae of many non-probabilistic behavioral relations. Our paper studies their techniques in a probabilistic setting. We provide a general method for determining characteristic formulae of behavioral relations for probabilistic automata using fixed-point probability logics. We consider such behavioral relations as simulations and bisimulations, probabilistic bisimulations, probabilistic weak simulations, and probabilistic forward simulations. This paper shows how their constructions and proofs can follow from a single common technique.

AB - Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae of many non-probabilistic behavioral relations. Our paper studies their techniques in a probabilistic setting. We provide a general method for determining characteristic formulae of behavioral relations for probabilistic automata using fixed-point probability logics. We consider such behavioral relations as simulations and bisimulations, probabilistic bisimulations, probabilistic weak simulations, and probabilistic forward simulations. This paper shows how their constructions and proofs can follow from a single common technique.

U2 - 10.1007/978-3-642-27940-9_26

DO - 10.1007/978-3-642-27940-9_26

M3 - Conference article

VL - 7148

SP - 396

EP - 411

JO - Lecture Notes in Computer Science

T2 - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -