scholarly journals Study of Photon Multiplicity Distribution in the Context of Negative Binomial Distribution

2021 ◽  
pp. 1418-1422
Author(s):  
Neeraj Gupta
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Multiplicity Distribution ◽  
Negative Binomial ◽  
Simulation Data ◽  
Standard Program ◽  
Free Parameters ◽  
Photon Multiplicity

The attempt is made to characterize the photon multiplicity distribution as recorded in Photon Multiplicity Detector (PMD) as a part of the WA98 experiment for Pb-Pb interactions at 158 A GeV in terms of Negative Binomial Distribution. The free parameters n ̅ and k of the negative binomial distribution are optimized using CERN standard program MINIUTE and the errors or the parameters are calculated as given in MINOS software. The results obtained are compared with the predictions of Monte Carlo simulation data using VENUS 4.12.

scholarly journals KAJIAN VARIANCE MEAN RATIO PADA SIMULASI SEBARAN DATA BINOMIAL NEGATIF

2020 ◽  
Vol 4 (4) ◽  
pp. 615-626
Author(s):  
Choirun Nisa ◽  
Muhammad Nur Aidi ◽  
I Made Sumertajaya
Keyword(s):  
Data Collection ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Data Distribution ◽  
P Value ◽  
Binomial Data ◽  
Simulation Data ◽  
Count Distribution

The negative binomial distribution is one of the data collection counts that focuses on success and failure events. This study conducted a study of the distribution of negative binomial data to determine the characterization of the distribution based on the value of Variance Mean Ratio (VMR). Simulation data are generated based on negative binomial distribution with a combination of p and n parameters. The results of the VMR study on negative binomial distribution simulation data show that the VMR value will be smaller when the p-value is large and the VMR value is more stable as the sample size increases. Simulation data of negative binomial distribution when p≥0.5 begins to change data distribution to the distribution of Poisson and binomial. The calculation VMR value can be used as a reference for detecting patterns of data count distribution.

CHOU-YANG MULTIPLICITY CORRELATIONS IN HIGH ENERGY MULTIPARTICLE PRODUCTION AND LHC PREDICTION

2009 ◽  
Vol 24 (18n19) ◽  
pp. 3552-3560 ◽  
Author(s):  
W. C. LAI ◽  
A. H. CHAN ◽  
C. H. OH
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Cluster Size ◽  
Multiplicity Distribution ◽  
Negative Binomial ◽  
High Energy ◽  
Hadron Collision ◽  
Correlation Parameter ◽  
Wide Range ◽  
Hadron Hadron Collision

A Chou-Yang Type multiplicity distribution which consists of a stochastic (binomial) component for z = nf - nb and a nonstochastic (Generalized Multiplicity Distribution) component for n = nf + nb, is used to analyze the forward-backward multipicity distributions for hadron hadron collision over a wide range of cms energy. The results are compared with the multiplicity distribution using the Negative Binomial Distribution. Finally, cluster size r = 3.30±0.14 and the correlation parameter b = 0.98 are predicted for 14 TeV.

NEGATIVE BINOMIAL DISTRIBUTION AND MULTIPLICITY DISTRIBUTIONS FOR e+e− AND $p\bar{p}$ COLLISIONS

1986 ◽  
Vol 01 (09) ◽  
pp. 553-556
Author(s):  
C.K. CHEW ◽  
S. DATÉ ◽  
D. KIANG
Keyword(s):  
Experimental Data ◽  
Charged Particle ◽  
Impact Parameter ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Multiplicity Distribution ◽  
Negative Binomial ◽  
Particle Multiplicity ◽  
Charged Particle Multiplicity ◽  
Multiplicity Distributions

The charged particle multiplicity distribution in [Formula: see text] collision is related to that of e+e− in a class of geometric models. The multiplicity distribution at a given impact parameter is taken to be a negative binomial distribution. The calculation agrees well with the experimental data.

scholarly journals KAJIAN SIMULASI OVERDISPERSI PADA REGRESI POISSON DAN BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA BALITA GIZI BURUK

2020 ◽  
Vol 4 (3) ◽  
pp. 484-497
Author(s):  
Puput Cahya Ambarwati ◽  
Indahwati Indahwati ◽  
Muhammad Nur Aidi
Keyword(s):  
Poisson Distribution ◽  
Spatial Data ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Real Data ◽  
P Value ◽  
Simulation Data ◽  
Response Variable

Geographic weighted regression (GWR) is one of the regression methods for spatial data. GWR with the response variable following the poisson distribution can use the geographic weighted poisson regression (GWPR). GWPR often does not complete the assumption of dispersion. The classic approach commonly used to overcome overdispersion is related to poisson distribution, which is the approach obtained from poisson and gamma distribution which is similar to negative binomial distribution function. GWR for the response variable following the negative binomial distribution can use the geographical weighted negative binomial regression (GWNBR). The data used in this study are simulation data and real data. The results of the simulation data are the tolerance limits that are still precisely modeled with GWPR are overdispersion approaching 1 based on significant amount and average p-value.. The results of research from real data, the GWNBR is the best model for overdispersion cases in malnourished children in East Java Province in 2017 compared to the GWPR based on comparison of the values ​​of AIC. 

PARTON EVOLUTION AND THE NEGATIVE BINOMIAL MULTIPLICITY DISTRIBUTION

1992 ◽  
Vol 07 (18) ◽  
pp. 1617-1622
Author(s):  
E.D. MALAZA ◽  
R.A. RITCHIE ◽  
H.G. MILLER
Keyword(s):  
Evolution Equation ◽  
Perturbative Qcd ◽  
Energy Dependence ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Multiplicity Distribution ◽  
Negative Binomial ◽  
Shape Parameters ◽  
Branching Model

Within a semiclassical parton branching model, we derive the evolution equation for the multiplicity distribution in hadron-hadron collisions based on a negative binomial distribution which contains the energy dependence from QCD. We deduce the perturbative QCD form of the shape parameters consistent with data in pp, [Formula: see text] and e+e− experiments.

Niche Sharing in Intertidal Mollusks and Decapods in Rocky Shore of Easter Island

2019 ◽  
Vol 53 (5) ◽  
pp. 417-422
Author(s):  
P. De los Ríos ◽  
E. Ibáñez Arancibia
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Rocky Shore ◽  
Negative Binomial ◽  
Null Model ◽  
Easter Island ◽  
Published Data ◽  
Distribution Analysis ◽  
Field Works

Abstract The coastal marine ecosystems in Easter Island have been poorly studied, and the main studies were isolated species records based on scientific expeditions. The aim of the present study is to apply a spatial distribution analysis and niche sharing null model in published data on intertidal marine gastropods and decapods in rocky shore in Easter Island based in field works in 2010, and published information from CIMAR cruiser in 2004. The field data revealed the presence of decapods Planes minutus (Linnaeus, 1758) and Leptograpsus variegatus (Fabricius, 1793), whereas it was observed the gastropods Nodilittorina pyramidalis pascua Rosewater, 1970 and Nerita morio (G. B. Sowerby I., 1833). The available information revealed the presence of more species in data collected in 2004 in comparison to data collected in 2010, with one species markedly dominant in comparison to the other species. The spatial distribution of species reported in field works revealed that P. minutus and N. morio have aggregated pattern and negative binomial distribution, L. variegatus had uniform pattern with binomial distribution, and finally N. pyramidalis pascua, in spite of aggregated distribution pattern, had not negative binomial distribution. Finally, the results of null model revealed that the species reported did not share ecological niche due to competition absence. The results would agree with other similar information about littoral and sub-littoral fauna for Easter Island.

ANALISIS RISIKO OPERASIONAL MENGGUNAKAN PENDEKATAN DISTRIBUSI KERUGIAN DENGAN METODE AGREGAT

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Financial Institution ◽  
Expected Value ◽  
Insurance Companies ◽  
Limited Data ◽  
Loss Distribution Approach ◽  
Potential Loss ◽  
The Impact

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.

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