The cost of a carrier-borne epidemic

1974 ◽  
Vol 11 (04) ◽  
pp. 642-651 ◽  
Author(s):  
D. Jerwood
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Expected Number ◽  
Stochastic Epidemic ◽  
Definition Of ◽  
The Cost ◽  
General Stochastic

In this paper, the cost of the carrier-borne epidemic is considered. The definition of duration, as used by Weiss (1965) and subsequent authors, is generalised and the probability distribution for the number of located carriers is obtained. One component of cost, namely the area generated by the trajectory of carriers, is examined and its probability density function derived. The expected area generated is then shown to be proportional to the expected number of carriers located during the epidemic, a result which has an analogue in the general stochastic epidemic.

The cost of a carrier-borne epidemic

1974 ◽  
Vol 11 (4) ◽  
pp. 642-651 ◽  
Author(s):  
D. Jerwood
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Expected Number ◽  
Stochastic Epidemic ◽  
Definition Of ◽  
The Cost ◽  
General Stochastic

In this paper, the cost of the carrier-borne epidemic is considered. The definition of duration, as used by Weiss (1965) and subsequent authors, is generalised and the probability distribution for the number of located carriers is obtained. One component of cost, namely the area generated by the trajectory of carriers, is examined and its probability density function derived. The expected area generated is then shown to be proportional to the expected number of carriers located during the epidemic, a result which has an analogue in the general stochastic epidemic.

Age Reporting of Very Old Samples

Radiocarbon ◽  
1980 ◽  
Vol 22 (4) ◽  
pp. 1021-1027 ◽  
Author(s):  
Adam Walanus ◽  
Mieczysław F Pazdur
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Statistical Interpretation ◽  
Very Old ◽  
Radiocarbon Age ◽  
Fiducial Probability ◽  
Limiting Value

Problems of the statistical interpretation of radiocarbon age measurements of old samples are discussed, based on the notion of fiducial probability distribution. A probability density function of age has been given. A detailed discussion of different facets of the probability distribution of age has led us to the confirmation of the use of 2σ as the best limiting value between the regions of finite and infinite dates. It has been proposed to make use of the principle of constant probability P = 0.68 in the regions of both finite and infinite ages instead of the criterion N + kσ.

AN EMPIRICAL STUDY OF THE PROBABILITY DENSITY FUNCTION OF HF NOISE (PART II)

2008 ◽  
Vol 08 (03n04) ◽  
pp. L305-L314 ◽  
Author(s):  
J. GIESBRECHT
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
South Australia ◽  
Noise Model ◽  
Modulation Recognition ◽  
Noise Sources ◽  
Noise Density ◽  
Automatic Modulation Recognition ◽  
Definition Of

The impetus for investigating the probability density function of high-frequency (HF) noise arises from the requirement for a better noise model for automatic modulation recognition techniques. Many current modulation recognition methods still assume Gaussian noise models for the transmission medium. For HF communications this can be an incorrect assumption. Whereas a previous investigation [1] focuses on the noise density function in an urban area of Adelaide Australia, this work studies the noise density function in a remote country location east of Adelaide near Swan Reach, South Australia. Here, the definition of HF noise is primarily of natural origins – it is therefore impulsive – and excludes man-made noise sources. A new method for measuring HF noise is introduced that is used over a 153 kHz bandwidth at various frequencies across the HF band. The method excises man-made signals and calculates the noise PDF from the residue. Indeed, the suitability of the Bi-Kappa distribution at modeling HF noise is found to be even more compelling than suggested by the results of the earlier investigation.

scholarly journals Maximum-likelihood estimation of the parameters of a four-parameter class of probability distributions

1988 ◽  
Vol 31 (2) ◽  
pp. 271-283 ◽  
Author(s):  
Siegfried H. Lehnigk
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Maximum Likelihood Estimation ◽  
Probability Density ◽  
Density Function ◽  
Probability Distributions ◽  
Likelihood Estimation ◽  
Initial Shape ◽  
Definition Of ◽  
Image Position

We shall concern ourselves with the class of continuous, four-parameter, one-sided probability distributions which can be characterized by the probability density function (pdf) classIt depends on the four parameters: shift c ∈ R, scale b > 0, initial shape p < 1, and terminal shape β > 0. For p ≦ 0, the definition of f(x) can be completed by setting f(c) = β/bΓ(β−1)>0 if p = 0, and f(c) = 0 if p < 0. For 0 < p < 1, f(x) remains undefined at x = c; f(x)↑ + ∞ as x↓c.

scholarly journals Appropriate Probability Density Function of Convex Bodies.

2021 ◽  
pp. 130-139
Author(s):  
Khalid A Ateia ◽  
Tarig A Abdelhaleem
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Convex Body ◽  
Probability Density ◽  
Density Function ◽  
Random Vector ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Convex Bodies ◽  
Concentration Of Measure

We investigate under the notion of Large Deviation Principle & Concentration of Measure as a technique,the ability of estimating the probability density function of any random vector in the space Rn. We found that an appropriate probability distribution for any convex body in the space is sub – Gaussian.

Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

2019 ◽  
Vol 892 ◽  
pp. 284-291
Author(s):  
Ahmed S.A. Badawi ◽  
Nurul Fadzlin Hasbullah ◽  
Siti Hajar Yusoff ◽  
Sheroz Khan ◽  
Aisha Hashim ◽  
...  
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Probability Distribution ◽  
Wind Energy ◽  
Probability Density ◽  
Density Function ◽  
Gaza Strip ◽  
Actual Data ◽  
Average Wind Speed ◽  
Average Wind

The need of clean and renewable energy, as well as the power shortage in Gaza strip with few wind energy studies conducted in Palestine, provide the importance of this paper. Probability density function is commonly used to represent wind speed frequency distributions for the evaluation of wind energy potential in a specific area. This study shows the analysis of the climatology of the wind profile over the State of Palestine; the selections of the suitable probability density function decrease the wind power estimation error percentage. A selection of probability density function is used to model average daily wind speed data recorded at for 10 years in Gaza strip. Weibull probability distribution function has been estimated for Gaza based on average wind speed for 10 years. This assessment is done by analyzing wind data using Weibull probability function to find out the characteristics of wind energy conversion. The wind speed data measured from January 1996 to December 2005 in Gaza is used as a sample of actual data to this study. The main aim is to use the Weibull representative wind data for Gaza strip to show how statistical model for Gaza Strip over ten years. Weibull parameters determine by author depend on the pervious study using seven numerical methods, Weibull shape factor parameter is 1.7848, scale factor parameter is 4.3642 ms-1, average wind speed for Gaza strip based on 10 years actual data is 2.95 ms-1 per a day so the behavior of wind velocity based on probability density function show that we can produce energy in Gaza strip.

scholarly journals Exploring Finite-Sized Scale Invariance in Stochastic Variability with Toy Models: The Ornstein–Uhlenbeck Model

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1927
Author(s):  
Nachiketa Chakraborty
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Scale Invariance ◽  
Finite Size ◽  
Fundamental Property ◽  
Cosmic Ray ◽  
Self Similarity ◽  
Definition Of ◽  
Stochastic Properties

Stochastic variability is ubiquitous among astrophysical sources. Quantifying stochastic properties of observed time-series or lightcurves, can provide insights into the underlying physical mechanisms driving variability, especially those of the particles that radiate the observed emission. Toy models mimicking cosmic ray transport are particularly useful in providing a means of linking the statistical analyses of observed lightcurves to the physical properties and parameters. Here, we explore a very commonly observed feature; finite sized self-similarity or scale invariance which is a fundamental property of complex, dynamical systems. This is important to the general theme of physics and symmetry. We investigate it through the probability density function of time-varying fluxes arising from a Ornstein–Uhlenbeck Model, as this model provides an excellent description of several time-domain observations of sources like active galactic nuclei. The probability density function approach stems directly from the mathematical definition of self-similarity and is nonparametric. We show that the OU model provides an intuitive description of scale-limited self-similarity and stationary Gaussian distribution while potentially showing a way to link to the underlying cosmic ray transport. This finite size of the scale invariance depends upon the decay time in the OU model.

scholarly journals Mathematical approach to human character

2020 ◽  
Author(s):  
Takuya Yabu
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Negative Emotions ◽  
The Other ◽  
Mathematical Approach ◽  
Fixed Probability ◽  
To Receive

I thought about whether to receive positive or negative emotions from an event from the perspective of human character. Regarding the human character, I define it as a process of selecting one's emotion x so that the received emotion x becomes x=0 with respect to the event X and the reaction of the other party when one's thoughts and reactions occur as the accompanying reactions. Mathematically modeled it, the probability density function of how much to select an emotion has a fixed probability distribution. I also described how to deal with one's character as an application of this model.

Renewal processes decomposable into i.i.d. components

1980 ◽  
Vol 12 (3) ◽  
pp. 672-688 ◽  
Author(s):  
Yoshifusa Ito
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Finite Number ◽  
Probability Density ◽  
Density Function ◽  
Renewal Process ◽  
Recurrence Relations ◽  
Renewal Processes ◽  
Supplementary Condition

Let N be a stationary renewal process with a probability density function f(t). Suppose that N can be expressed as the superposition of a finite number of i.i.d. stationary components N(1), …, N(p) (p≧2). Then, under a supplementary condition on f(t), N and N(1), …, N(p) are all Poisson. This is proved by using recurrence relations given in Ito (1978) for the probability distribution of i.i.d. components of a superposition process.

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