Discrete random Processes

1949 ◽  
Vol 8 (04) ◽  
pp. 204-209
Keyword(s):  
Random Process ◽  
Random Variable ◽  
Random Processes ◽  
Discrete Random Variable ◽  
Pure Mathematician ◽  
Discrete Values

If a variable assumes the discrete valuesxj(j= 1, 2, 3, …) with specified probabilitiesf(xj), wheref(xj) it is said to be adiscrete random variable. If a discrete random variable is also a function of a continuous (non-random) variable, for convenience usually assumed to be ‘time’, it is called adiscrete random process.A class of discrete random processes of particular interest to the pure mathematician and to the mathematical statistician has been calledstochastically definiteby Kolmogoroff (1931). Such a random process is distinguished by the fact that the probability that the random variable concerned assumes a given valuenat timetdepends only on the valuemassumed by the variable at times(s < t) and not on the values assumed at any intermediate or earlier points of time. This circumstance is allowed for by writing the probability of the valuenat timetin the form Pmn(s, t).

scholarly journals A Method of Predicting Risk of Natural Emergencies on Road Network

2015 ◽  
Vol 4 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Трофименко ◽  
Yuri Trofimenko ◽  
Якубович ◽  
A. Yakubovich
Keyword(s):  
Random Process ◽  
Road Network ◽  
Information Technologies ◽  
Random Variable ◽  
Transportation Infrastructure ◽  
Main Difficulty ◽  
Discrete Random Variable ◽  
Emergency Situations ◽  
Environmental Surveys

The main difficulty of describing natural emergency as a random process is the large number of parameters that must be quantified. Authors suggest threating the onset of emergency as a discrete random variable; each possible implementation corresponds to the defined size of the expected damage to transportation infrastructure. The analysis of the engineering and environmental surveys via geo-information technologies identified expected probability of occurrence and scale of the annual damage for 10 types of emergency situations on long-term (up to 2030) for State Company Russian Highways road network.

scholarly journals Random additions in urns of integers

2021 ◽  
Vol 58 (2) ◽  
pp. 335-346
Author(s):  
Mackenzie Simper
Keyword(s):  
Finite Group ◽  
Random Process ◽  
Uniform Distribution ◽  
Discrete Time ◽  
Random Variable ◽  
Urn Model ◽  
Infinite Type ◽  
The Mean ◽  
A Finite Group ◽  
Proof Of Convergence

AbstractConsider an urn containing balls labeled with integer values. Define a discrete-time random process by drawing two balls, one at a time and with replacement, and noting the labels. Add a new ball labeled with the sum of the two drawn labels. This model was introduced by Siegmund and Yakir (2005) Ann. Prob.33, 2036 for labels taking values in a finite group, in which case the distribution defined by the urn converges to the uniform distribution on the group. For the urn of integers, the main result of this paper is an exponential limit law. The mean of the exponential is a random variable with distribution depending on the starting configuration. This is a novel urn model which combines multi-drawing and an infinite type of balls. The proof of convergence uses the contraction method for recursive distributional equations.

scholarly journals A note on characteristic function for Bernstein polynomials involving special numbers and polynomials

Filomat ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 543-549
Author(s):  
Buket Simsek
Keyword(s):  
Characteristic Function ◽  
Binomial Distribution ◽  
Bernstein Polynomials ◽  
Random Variable ◽  
Stirling Numbers ◽  
Bernoulli Numbers ◽  
Discrete Random Variable ◽  
Euler Numbers ◽  
Negative Order ◽  
Special Numbers And Polynomials

The aim of this present paper is to establish and study generating function associated with a characteristic function for the Bernstein polynomials. By this function, we derive many identities, relations and formulas relevant to moments of discrete random variable for the Bernstein polynomials (binomial distribution), Bernoulli numbers of negative order, Euler numbers of negative order and the Stirling numbers.

Modified Mixed Generalized Ordered Response Model to Handle Misclassification in Injury Severity Data

2018 ◽  
Vol 2672 (30) ◽  
pp. 53-63 ◽  
Author(s):  
Lacramioara Balan ◽  
Rajesh Paleti
Keyword(s):  
Injury Severity ◽  
Random Variable ◽  
Parameter Estimates ◽  
Discrete Random Variable ◽  
Response Models ◽  
Ordinal Response ◽  
Proposed Model ◽  
Misclassification Errors ◽  
Misclassification Rates ◽  
Ordered Response

Traditional crash databases that record police-reported injury severity data are prone to misclassification errors. Ignoring these errors in discrete ordered response models used for analyzing injury severity can lead to biased and inconsistent parameter estimates. In this study, a mixed generalized ordered response (MGOR) model that quantifies misclassification rates in the injury severity variable and adjusts the bias in parameter estimates associated with misclassification was developed. The proposed model does this by considering the observed injury severity outcome as a realization from a discrete random variable that depends on true latent injury severity that is unobservable to the analyst. The model was used to analyze misclassification rates in police-reported injury severity in the 2014 General Estimates System (GES) data. The model found that only 68.23% and 62.75% of possible and non-incapacitating injuries were correctly recorded in the GES data. Moreover, comparative analysis with the MGOR model that ignores misclassification not only has lower data fit but also considerable bias in both the parameter and elasticity estimates. The model developed in this study can be used to analyze misclassification errors in ordinal response variables in other empirical contexts.

Extreme Values of Waves and Ship Responses Subject to the Markov Chain Condition

1979 ◽  
Vol 23 (03) ◽  
pp. 188-197
Author(s):  
Michel K. Ochi
Keyword(s):  
Distribution Function ◽  
Markov Chain ◽  
Probability Distribution ◽  
Random Process ◽  
Probability Distribution Function ◽  
Extreme Values ◽  
Random Processes ◽  
Chain Condition ◽  
Extreme Value ◽  
Spectral Bandwidth

This paper discusses the effect of statistical dependence of the maxima (peak values) of a stationary random process on the magnitude of the extreme values. A theoretical analysis of the extreme values of a stationary normal random process is made, assuming the maxima are subject to the Markov chain condition. For this, the probability distribution function of maxima as well as the joint probability distribution function of two successive maxima of a normal process having an arbitrary spectral bandwidth are applied to Epstein's theorem for evaluating the extreme values in a given sample under the Markov chain condition. A numerical evaluation of the extreme values is then carried out for a total of 14 random processes, including nine ocean wave records, with various spectral bandwidth parameters ranging from 0.11 to 0.78. From the results of the computations, it is concluded that the Markov concept is applicable to the maxima of random processes whose spectral bandwidth parameter, ɛ, is less than 0.5, and that the extreme values with and without the Markov concept are constant irrespective of the e-value, and the former is approximately 10 percent greater than the latter. It is also found that the sample size for which the extreme value reaches a certain level with the Markov concept is much less than that without the Markov concept. For example, the extreme value will reach a level of 4.0 (nondimensional value) in 1100 observations of the maxima with the Markov concept, while the extreme value will reach the same level in 3200 observations of the maxima without the Markov concept.

On the two sided barrier problem

1965 ◽  
Vol 2 (01) ◽  
pp. 79-87
Author(s):  
Masanobu Shinozuka
Keyword(s):  
Random Process ◽  
Ground Motion ◽  
Lower Bounds ◽  
Present Method ◽  
Structural Response ◽  
Random Processes ◽  
Upper And Lower Bounds ◽  
Numerical Examples ◽  
Long Run ◽  
Barrier Problem

Upper and lower bounds are given for the probability that a separable random process X(t) will take values outside the interval (— λ 1, λ 2) for 0 ≦ t ≦ T, where λ 1 and λ 2 are positive constants. The random process needs to be neither stationary, Gaussian nor purely random (white noise). In engineering applications, X(t) is usually a random process decaying with time at least in the long run such as the structural response to the acceleration of ground motion due to earthquake. Numerical examples show that the present method estimates the probability between the upper and lower bounds which are sufficiently close to be useful when the random processes decay with time.

Uniform discrete random-variable generator

1973 ◽  
Vol 11 (3) ◽  
pp. 362-364 ◽  
Author(s):  
P. A. Parker ◽  
R. N. Scott
Keyword(s):  
Random Variable ◽  
Discrete Random Variable

On certain coloring parameters of Mycielski graphs of some graphs

2018 ◽  
Vol 10 (03) ◽  
pp. 1850030
Author(s):  
N. K. Sudev ◽  
K. P. Chithra ◽  
K. A. Germina ◽  
S. Satheesh ◽  
Johan Kok
Keyword(s):  
Graph Coloring ◽  
Random Variable ◽  
Proper Coloring ◽  
Discrete Random Variable ◽  
Variable Formula ◽  
Statistical Measures ◽  
Random Experiment ◽  
Mean And Variance

Coloring the vertices of a graph [Formula: see text] according to certain conditions can be considered as a random experiment and a discrete random variable [Formula: see text] can be defined as the number of vertices having a particular color in the proper coloring of [Formula: see text]. The concepts of mean and variance, two important statistical measures, have also been introduced to the theory of graph coloring and determined the values of these parameters for a number of standard graphs. In this paper, we discuss the coloring parameters of the Mycielskian of certain standard graphs.

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