Monte-Carlo simulations of atmospheric muon production: Implication of the past martian environment

Icarus ◽  
2007 ◽  
Vol 191 (2) ◽  
pp. 603-615 ◽  
Author(s):  
Hiroyuki K.M. Tanaka
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
The Past ◽  
Muon Production ◽  
Atmospheric Muon

scholarly journals Monte Carlo simulations of muon production

1996 ◽  
Author(s):  
Robert B. Palmer ◽  
Juan C. Gallardo ◽  
Richard C. Fernow ◽  
Yaǧmur Torun ◽  
David Neuffer ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Muon Production

The Use of Fisher’s Z in Schmidt-Hunter–type Meta-Analyses

1995 ◽  
Vol 20 (3) ◽  
pp. 287-306 ◽  
Author(s):  
Kenneth S. Law
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Monte Carlo Simulations ◽  
Pearson Correlation ◽  
Positive Bias ◽  
The Past ◽  
New Methods ◽  
Population Correlation ◽  
Meta Analyses

Over the past few years, there has been some dispute as to whether Fisher’s Z or the Pearson correlation (r) should be used in Schmidt-Hunter–type meta-analyses. The two major reasons that Z has not been recommended are the possibly larger positive bias in estimating mean population correlation (Mρ) and the problem of estimating the standard deviation of population correlations (SDρ). In this study, two new methods of estimating Mρ and SDρ by using Z are suggested and tested by Monte Carlo simulations. The results show no consistent advantage to using r instead of Z or vice versa in estimating both Mρ and SDρ In fact, the estimated Mρ and SDρ from all three methods were highly similar and were almost identical when rounded off.

Simulations of the Domain State Model

2002 ◽  
Vol 746 ◽  
Author(s):  
U. Nowak ◽  
A. Misra ◽  
K. D. Usadel
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Exchange Bias ◽  
Ferromagnetic Layer ◽  
State Model ◽  
Positive Bias ◽  
Initial Field ◽  
The Past ◽  
Domain State ◽  
Antiferromagnetic Layer

ABSTRACTThe domain state model for exchange bias consists of a ferromagnetic layer exchange coupled to an antiferromagnetic layer. In order to model a certain degree of disorder within the bulk of the antiferromagnet, the latter is diluted throughout its volume. Extensive Monte Carlo simulations of the model were performed in the past. Exchange bias is observed as a result of a domain state in the antiferromagnetic layer which develops during the initial field cooling, carrying a remanent domains state magnetization which is partly irreversible during hysteresis. A variety of typical effects associated with exchange bias like, e. g., its dependence on dilution, positive bias, temperature and time dependences as well as the dependence on the thickness of the antiferromagnetic layer can be explained within this model.

On Guessing: An Alternative Adjusted Positive Learning Estimator and Comparing Probability Misspecification With Monte Carlo Simulations

2021 ◽  
pp. 014662162110139
Author(s):  
Ben O. Smith ◽  
Dustin R. White
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Test Score ◽  
Recursive Partitioning ◽  
True Positive ◽  
Learning Gain ◽  
Estimation Techniques ◽  
The Past ◽  
Post Test ◽  
Simulation Results

Practitioners in the sciences have used the “flow” of knowledge (post-test score minus pre-test score) to measure learning in the classroom for the past 50 years. Walstad and Wagner, and Smith and Wagner moved this practice forward by disaggregating the flow of knowledge and accounting for student guessing. These estimates are sensitive to misspecification of the probability of guessing correct. This work provides guidance to practitioners and researchers facing this problem. We introduce a transformed measure of true positive learning that under some knowable conditions performs better when students’ ability to guess correctly is misspecified and converges to Hake’s normalized learning gain estimator under certain conditions. We then use simulations to compare the accuracy of two estimation techniques under various violations of the assumptions of those techniques. Using recursive partitioning trees fitted to our simulation results, we provide the practitioner concrete guidance based on a set of yes/no questions.

Monte Carlo Simulations and Semianalytical Parameteri-Sations of the Atmospheric Muon Flux

2006 ◽  
pp. 399-402
Author(s):  
Bogdan Mitrica ◽  
Iliana Brancus ◽  
Gabriel Toma ◽  
Juergen Wentz ◽  
Heinigerd Rebel ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Muon Flux ◽  
Atmospheric Muon

scholarly journals Monte Carlo simulations of muon production

10.2172/46704 ◽  
1995 ◽  
Author(s):  
R.B. Palmer ◽  
J.C. Gallardo ◽  
R.C. Fernow ◽  
Y. Torun ◽  
D. Neuffer ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Muon Production

Widening and clustering techniques allowing the use of monotone CFTP algorithm

2015 ◽  
Vol 21 (4) ◽  
Author(s):  
Mohamed Yasser Bounnite ◽  
Abdelaziz Nasroallah
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Simulations ◽  
State Space ◽  
Stationary Distribution ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
The Past ◽  
Coupling From The Past

AbstractThe standard Coupling From The Past (CFTP) algorithm is an interesting tool to sample from exact stationary distribution of a Markov chain. But it is very expensive in time consuming for large chains. There is a monotone version of CFTP, called MCFTP, that is less time consuming for monotone chains. In this work, we propose two techniques to get monotone chain allowing use of MCFTP: widening technique based on adding two fictitious states and clustering technique based on partitioning the state space in clusters. Usefulness and efficiency of our approaches are showed through a sample of Markov Chain Monte Carlo simulations.

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