A time-series model for Laplace (double-exponential) variables having second-order autoregressive structure (NLAR(2)) is presented. The model is Markovian and extends the second-order process in exponential variables, NEAR(2), to the case where the marginal distribution is Laplace. The properties of the Laplace distribution make it useful for modeling in some cases where the normal distribution is not appropriate. The time-series model has four parameters and is easily simulated. The autocorrelation function for the process is derived as well as third-order moments to further explore dependency in the process. The model can exhibit a broad range of positive and negative correlations and is partially time reversible. Joint distributions and the distribution of differences are presented for the first-order case NLAR(1).

A new variance reduction technique is introduced called internal control variables, to be used in the context of regeneration simulations. The idea is to identify a sequence of control random variables, each one defined within a regenerative cycle, whose mean can be calculated analytically. These controls should be highly correlated with the usual quantities observed in a regenerative simulation. This correlation reduces the variance of the estimate for the parameter of interest. Numerical examples are included for the waiting time process of an M/M/1 queue and for several Markov chains. (Author)

This paper considers some of the issues that arise when messages or jobs inbound to a computer facility are buffered prior to being processed.Models are developed that describe (a) the results of blocking a single memory unit for the use of diverse messages, (b) the occupancy behavior of a buffer that is tied to a single message source, and (c) the occupancy of a buffer dynamically shared among many independent sources.

article Free Access Share on A Cyclic-Queue Model of System Overhead in Multiprogrammed Computer Systems Authors: P. A. W. Lewis IBM Thomas J. Watson Research Center, Yorktown Heights, New York and Imperial College, University of London IBM Thomas J. Watson Research Center, Yorktown Heights, New York and Imperial College, University of LondonView Profile , G. S. Shedler IBM Research Center, San Jose, California IBM Research Center, San Jose, CaliforniaView Profile Authors Info & Claims Journal of the ACMVolume 18Issue 2April 1971 pp 199–220https://doi.org/10.1145/321637.321642Published:01 April 1971Publication History 33citation784DownloadsMetricsTotal Citations33Total Downloads784Last 12 Months11Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A basic hypothesis is stated about the contextual and co-occurrence properties of synonymous words. On the basis of this hypothesis, several statistics are derived for use in discriminating between pairs of words which are synonymous and pairs of words which are nonsynonymous. The discriminating power of these statistics is tested on a corpus consisting of titles of physics theses. The tests indicate that two of the derived statistics have relatively high discriminating power. The results are interpreted and the possibility of obtaining better discriminating power is discussed.