scholarly journals Techno-economic process modelling and Monte Carlo simulation data of uncertainty quantification in field-grown plant-based manufacturing

Data in Brief ◽  
2021 ◽  
pp. 107317
Author(s):  
Matthew J. McNulty ◽  
Kirolos Kelada ◽  
Debashis Paul ◽  
Somen Nandi ◽  
Karen A. McDonald
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Process Modelling ◽  
Simulation Data ◽  
Economic Process ◽  
Grown Plant

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 Commuting-Adjusted Short-Term Health Impact Assessment of Airborne Fine Particles with Uncertainty Quantification via Monte Carlo Simulation

2015 ◽  
Vol 123 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Michela Baccini ◽  
Laura Grisotto ◽  
Dolores Catelan ◽  
Dario Consonni ◽  
Pier Alberto Bertazzi ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Impact Assessment ◽  
Uncertainty Quantification ◽  
Health Impact Assessment ◽  
Fine Particles ◽  
Health Impact ◽  
Short Term

BGK contamination model of the MSX satellite and comparison with Monte-Carlo simulation data

1992 ◽  
Author(s):  
Paul T. Ma ◽  
Loren C. Zumwalt ◽  
Francis C. Wang ◽  
Shirley C. Yfantis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Data

scholarly journals PERHITUNGAN VaR PORTOFOLIO SAHAM MENGGUNAKAN DATA HISTORIS DAN DATA SIMULASI MONTE CARLO

2012 ◽  
Author(s):  
WAYAN ARTHINI ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Stock Returns ◽  
Value At Risk ◽  
Time Horizon ◽  
Historical Data ◽  
Portfolio Risk ◽  
Simulation Data ◽  
Risk And Return

Value at Risk (VaR) is the maximum potential loss on a portfolio based on the probability at a certain time.  In this research, portfolio VaR values calculated from historical data and Monte Carlo simulation data. Historical data is processed so as to obtain stock returns, variance, correlation coefficient, and variance-covariance matrix, then the method of Markowitz sought proportion of each stock fund, and portfolio risk and return portfolio. The data was then simulated by Monte Carlo simulation, Exact Monte Carlo Simulation and Expected Monte Carlo Simulation. Exact Monte Carlo simulation have same returns and standard deviation  with historical data, while the Expected Monte Carlo Simulation satistic calculation similar to historical data. The results of this research is the portfolio VaR  with time horizon T=1, T=10, T=22 and the confidence level of 95 %, values obtained VaR between historical data and Monte Carlo simulation data with the method exact and expected. Value of VaR from both Monte Carlo simulation is greater than VaR historical data.

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