Investigation of contribution of number of trials in Monte Carlo simulation for uncertainty estimation for a pressure balance

2021 ◽  
pp. 014233122110390
Author(s):  
Jasveer Singh ◽  
Neha Bura ◽  
Kapil Kaushik ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Nita Dilawar Sharma
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Critical Factor ◽  
Random Number Generation ◽  
Effective Area ◽  
National Physical Laboratory ◽  
Conventional Technique ◽  
Random Numbers ◽  
Pressure Balance

It is well established that the estimation of measurement uncertainty is vital for the validation of any measurement and is an essential parameter of quality assurance. Apart from the conventional technique of law of propagation of uncertainty (LPU), which has many limitations, Monte Carlo simulation (MCS) technique has become an essential tool for the estimation of measurement uncertainty in various fields of metrology. The most critical factor in MCS is the generation of random numbers of the input quantities according to their probability distributions. The number of Monte Carlo trials to generate these random numbers significantly affects the results. In particular, the required number of trials is also affected by the parameter for which the uncertainty is to be estimated. Hence, in the current paper, the effect of selection of the number of trials on the random number generation and the resulting output in terms of standard deviation (SD) is investigated for the uncertainty in the effective area of a pneumatic reference pressure standard (NPLI-4) at the CSIR-National Physical Laboratory of India. The simulation results thus obtained are compared amongst themselves, with an adaptive approach as well as with the experimental results. The outcomes are analyzed and discussed in detail.

Estimation of primary pH measurement uncertainty using Monte Carlo simulation

Metrologia ◽  
2006 ◽  
Vol 43 (3) ◽  
pp. 306-310 ◽  
Author(s):  
J C Damasceno ◽  
R M H Borges ◽  
P R G Couto ◽  
A P Ordine ◽  
M A Getrouw ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Ph Measurement ◽  
Primary Ph Measurement

Comparative evaluation of estimation of step gauge measurement uncertainty via Monte Carlo simulation

2019 ◽  
Vol 55 ◽  
pp. 390-396 ◽  
Author(s):  
Yuka Miura ◽  
Shoichi Nakanishi ◽  
Eiichi Higuchi ◽  
Kiyoshi Takamasu ◽  
Makoto Abe ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Comparative Evaluation ◽  
Gauge Measurement

scholarly journals Analisis Risiko Finansial Dengan Metode Simulasi Monte Carlo (Studi Kasus: Pt. Phase Delta Control)

2016 ◽  
Vol 8 (1) ◽  
pp. 62
Author(s):  
Atikah Aghdhi Pratiwi ◽  
Rosa Rilantiana
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Historical Data ◽  
Uncertain Variable ◽  
Random Numbers ◽  
Selling Price ◽  
Sales Volume ◽  
Simulation Research ◽  
Cost Of Production ◽  
A Company

AbstractBasically, the purpose of a company is make a profit and enrich the owners of the company. This is manifested by development and achievement of good performance, both in financial and operational perspective. But in reality, not all of companies can achieve good performance. One of them is because exposure of risk. This could threaten achievement of the objectives and existence of the company. Therefore, companies need to have an idea related to possible condition and financial projection in future periods that are affected by risk. One of the possible method is Monte Carlo Simulation. Research will be conducted at PT. Phase Delta Control with historical data related to production/sales volume, cost of production and selling price. Historical data will be used as Monte Carlo Simulation with random numbers that describe probability of each risk variables describing reality. The main result is estimated profitability of PT. Phase Delta Control in given period. Profit estimation will be uncertain variable due to some uncertainty

Assessing Curriculum Efficiency Through Monte Carlo Simulation

2018 ◽  
Vol 22 (4) ◽  
pp. 597-610
Author(s):  
David Torres ◽  
Jorge Crichigno ◽  
Carmella Sanchez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Distribution ◽  
Relative Frequency ◽  
Monte Carlo Algorithm ◽  
Pass Rate ◽  
Random Numbers ◽  
Pass Rates ◽  
Low Pass ◽  
Relative Frequency Distribution

A Monte Carlo algorithm is designed to predict the average time to graduate by enrolling virtual students in a degree plan. The algorithm can be used to improve graduation rates by identifying bottlenecks in a degree plan (e.g., low pass rate courses and prerequisites). Random numbers are used to determine whether students pass or fail classes by comparing them to institutional pass rates. Courses cannot be taken unless prerequisites and corequisites are satisfied. The output of the algorithm generates a relative frequency distribution which plots the number of students who graduate by semester. Pass rates of courses can be changed to determine the courses that have the greatest impact on the time to graduate. Prerequisites can also be removed to determine whether certain prerequisites significantly affect the time to graduate.

scholarly journals Simulasi Monte Carlo dalam Memprediksi Penerimaan Peserta Pelatihan Dasar CPNS

2021 ◽  
pp. 134-138
Author(s):  
Faisal Roza ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Civil Servant ◽  
Random Numbers ◽  
Basic Training ◽  
Internal Affairs

The implementation of basic training recruit (latsar) of civil servant (CPNS) at Pusat Pengembangan Sumber Daya Manusia (PPSDM) Ministry of Internal Affairs regional Bukittinggi. The leader takes decision in doing the implementation of latsar CPNS recruit in PPSDM scope regional Bukittinggi. Latsar CPNS is one of requirements to be civil servant. Therefore, it is necessary to collect data by doing observation, interview questionings with related party in the implementation of latsar CPNS recruit from 2018 to 2020. It can be predicted for the next recruit. After doing library references by reading some books and journals, the basic training recruit of CPNS sources from PPSDM regional Bukittinggi, and Monte Carlo simulation. By using Monte Carlo simulation in predicting data, it can get closer value of actual value. Based on distribution of sampling data, the method is by choosing random numbers from probability distribution to do simulation. The Monte Carlo result’s examination has got 173 participants for year 2019, 158 participants for year 2020, and 157 participants for year 2021 clearly. Although the rate of the accurate just reaches 81%, but it has been able to be recommended to help PPSDM regional Bukittinggi, Ministry of Internal Affairs in taking decision and planning for basic training recruit of CPNS for the next.

scholarly journals PERHITUNGAN VALUE AT RISK (VaR) DENGAN METODE HISTORIS DAN MONTE CARLO PADA SAHAM SUB SEKTOR ROKOK

2018 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Achmad Dimas ◽  
Muhammad Azhari ◽  
Khairunnisa Khairunnisa
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Time Series Data ◽  
Random Number Generation ◽  
Simulation Method ◽  
Series Data ◽  
Historical Simulation ◽  
Historical Method ◽  
Sample Data

The government’s policy, the Indonesian Ulema Council’s (MUI) fatwa, the rise of cigarette issues and anti-smoking campaigns have been a major challenge for the tobacco industry in managing risks. Through this research, the issues will be measured by VaR to know the risk of the company’s shares of cigarette sub sector by using time series data and analyzed by using the simulation method of Historis and Monte Carlo. The results showed the VaR value of GGRM and HMSP stock with the historical method is 3.28 and 2.54%. While the value of VaR shares GGRM and HMSP with Monte Carlo method is 3.52% and 3.14%. Monte Carlo simulation gives greater result than Historical Simulation, because Monte Carlo simulation do iteration repeatedly by involving random number generation and many synthesize the data so that sample data becomes more which makes the calculation is bigger.

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