scholarly journals Discrete-time classical and quantum Markovian evolutions: Maximum entropy problems on path space

2010 ◽  
Vol 51 (4) ◽  
pp. 042104 ◽  
Author(s):  
Michele Pavon ◽  
Francesco Ticozzi
Keyword(s):  
Maximum Entropy ◽  
Discrete Time ◽  
Path Space

Choice of operator length for maximum entropy spectral analysis

Geophysics ◽  
1978 ◽  
Vol 43 (7) ◽  
pp. 1384-1391 ◽  
Author(s):  
James G. Berryman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Empirical Evidence ◽  
Discrete Time ◽  
Seismic Data ◽  
A Priori ◽  
Evidence Based ◽  
Maximum Entropy Spectral Analysis ◽  
Discrete Time Series

Empirical evidence based on maximum entropy spectra of real seismic data suggests that M = 2N/ln 2N is a reasonable a priori choice of the operator length M for discrete time series of length N. Various examples support this conclusion.

MAXIMUM ENTROPY AND DISCRETE TIME SOFTWARE RELIABILITY GROWTH MODELS

2010 ◽  
Vol 17 (06) ◽  
pp. 587-601
Author(s):  
PANLOP ZEEPHONGSEKUL ◽  
SHIGERU YAMADA
Keyword(s):  
Maximum Entropy ◽  
Discrete Time ◽  
Software Reliability ◽  
Growth Models ◽  
Preliminary Investigation ◽  
Maximum Entropy Principle ◽  
Reliability Growth ◽  
Entropy Principle ◽  
Software Reliability Growth ◽  
Interpolation Polynomials

This paper provides a preliminary investigation into the application of the Maximum Entropy Principle (MEP), introduced by Jaynes in 1957, in modeling discrete time Software Reliability Growth Model (SRGM). On their own, each of these two topics are interesting with extensive applications, and here we will show how they can be combined to provide yet another application of the MEP among a huge array of proven successful applications. A brief discussion of MEP and SRGM will be given and a hitherto unnoticed relationship between MEP distribution and the Lagrange interpolation polynomials highlighted. We then show how MEP can be used to obtain some important distributions arising from discrete time SRGM. Finally, a simple example is given to illustrate the theory.

scholarly journals Maximum entropy properties of discrete-time first-order stable spline kernel

Automatica ◽  
2016 ◽  
Vol 66 ◽  
pp. 34-38 ◽  
Author(s):  
Tianshi Chen ◽  
Tohid Ardeshiri ◽  
Francesca P. Carli ◽  
Alessandro Chiuso ◽  
Lennart Ljung ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Discrete Time ◽  
First Order

scholarly journals Performance analysis of a threshold-based discrete-time queue using maximum entropy

2009 ◽  
Vol 17 (3) ◽  
pp. 558-568 ◽  
Author(s):  
L. Guan ◽  
I.U. Awan ◽  
I. Phillips ◽  
A. Grigg ◽  
W. Dargie
Keyword(s):  
Performance Analysis ◽  
Maximum Entropy ◽  
Discrete Time ◽  
Discrete Time Queue

Stochasting modeling of planetary ring systems

1984 ◽  
Vol 75 ◽  
pp. 461-469 ◽  
Author(s):  
Robert W. Hart
Keyword(s):  
Maximum Entropy ◽  
Ring System ◽  
The Sun ◽  
Ultimate State ◽  
Interparticle Interactions ◽  
Ring Systems ◽  
First Order ◽  
Planetary Ring ◽  
Insight Into

ABSTRACTThis paper models maximum entropy configurations of idealized gravitational ring systems. Such configurations are of interest because systems generally evolve toward an ultimate state of maximum randomness. For simplicity, attention is confined to ultimate states for which interparticle interactions are no longer of first order importance. The planets, in their orbits about the sun, are one example of such a ring system. The extent to which the present approximation yields insight into ring systems such as Saturn's is explored briefly.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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