Results 231 to 240 of about 127,543 (265)
Some of the next articles are maybe not open access.
Stopping sets and the girth of Tanner graphs
Proceedings IEEE International Symposium on Information Theory,, 2003Recent work has related the error probability of iterative decoding over erasure channels to the presence of stopping sets in the Tanner graph of the code used. In particular, it was shown that the smallest number of uncorrected erasures is the size of the graph's smallest stopping set. Relating stopping sets and girths, we consider the size /spl sigma/
A. Orlitsky +3 more
openaire +1 more source
A Statistical Method for Setting Stops in Stock Trading
Operations Research, 1970This paper applies the exponential distribution to stock price reactions to determine, at three confidence levels, the critical percentage price reaction beyond which a reaction constitutes a strong likelihood of a major reversal or halt in the stock's present general price trend. We show that these critical values can be used to determine stop losses,
openaire +2 more sources
Constructions of Fail-Stop Signatures for Multi-signer Setting
2015 10th Asia Joint Conference on Information Security, 2015Fail-stop signatures (FSS) provide the security for a signer against a computationally unbounded adversary by enabling the signer to provide a proof of forgery. Conventional FSS schemes are for a single-signer setting, but in the real world, there is a case where a countersignature of multiple signers (e.g.
Nobuaki Kitajima +4 more
openaire +1 more source
Stopping sets: Gamma-type results and hitting properties
Advances in Applied Probability, 1999Recently in the paper by Møller and Zuyev (1996), the following Gamma-type result was established. Given n points of a homogeneous Poisson process defining a random figure, its volume is Γ( n ,λ) distributed, where λ is the intensity of the process.
openaire +2 more sources
Optional stopping: No problem for Bayesians
Psychonomic Bulletin and Review, 2014Jeffrey N. Rouder
exaly
Automatic early stopping using cross validation: quantifying the criteria
Neural Networks, 1998Lutz Prechelt
exaly

