Monte‐Carlo analysis of two logical premises to avoid the Probit algorithm for determination of sensory threshold by psychophysics

2016 ◽  
Vol 139 (4) ◽  
pp. 2100-2100
Author(s):  
Amitava Biswas
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis ◽  
Sensory Threshold

Digital simulation of space missions for Monte Carlo analysis

SIMULATION ◽  
1968 ◽  
Vol 11 (3) ◽  
pp. 133-144 ◽  
Author(s):  
Frank J. Mullin
Keyword(s):  
Monte Carlo ◽  
Digital Simulation ◽  
Statistical Error ◽  
Analysis Data ◽  
Monte Carlo Analysis ◽  
Mathematical Treatment ◽  
Reference Trajectory ◽  
Complete Space ◽  
Param Eters

This paper describes a set of computer programs devel oped for Monte Carlo analysis of a complete space mis sion. The results of such an analysis are used in mission planning (for example, in the determination of how various errors affect the trajectory, midcourse-correction fuel re quirements, and other parameters). In Monte Carlo analysis, a sample mission is simulated a large number of times, with the various stochastic pa rameters assuming different random values for each run. The resulting data is then processed statistically to yield useful probabilistic statements regarding output param eters of interest. Three programs are described. The first derives, edits, and formats the input data required for simulation of a particular mission. The second is the simulation proper, generating a set of output parameters for each run. The third program processes the data from the simulation to derive the required statistical error-analysis data. The first and third programs are relatively straightforward and are briefly described; the simulation is described in more detail. In general, the simulation operates by ordering all the events of the mission and using a set of standard mathe matical descriptions called PROP boxes to compute the state vector at the end of each event from its state at the beginning of the event. It uses the deviations of the pa rameters from those of a reference trajectory rather than the values of the parameters themselves, thereby simplify ing the mathematical treatment and reducing the comput ing time by orders of magnitude.

Monte-Carlo Analysis of the Accuracy of a Novel Thomson Scattering Lidar Approach for Determination of the Electron Temperature in Fusion Plasmas

2010 ◽  
Author(s):  
Tanja N. Dreischuh ◽  
Ljuan L. Gurdev ◽  
Dimitar V. Stoyanov ◽  
Angelos Angelopoulos ◽  
Takis Fildisis
Keyword(s):  
Monte Carlo ◽  
Electron Temperature ◽  
Monte Carlo Analysis ◽  
Thomson Scattering ◽  
Fusion Plasmas

Reverse Monte Carlo Analysis of Powder Patterns

1996 ◽  
Vol 29 (3) ◽  
pp. 285-290 ◽  
Author(s):  
W. Montfrooij ◽  
R. L. McGreevy ◽  
R. Hadfield ◽  
N. H. Andersen
Keyword(s):  
Monte Carlo ◽  
Bragg Peak ◽  
Monte Carlo Analysis ◽  
Monte Carlo Algorithm ◽  
Reverse Monte Carlo ◽  
Peak Intensity ◽  
Thermal Displacements ◽  
Diffraction Patterns ◽  
Best Fit

A Monte Carlo algorithm for the analysis of powder diffraction patterns is presented. One aim of this algorithm, which can be used as a supplement to the regular Rietveld refinement, is to provide a self-consistent determination of the thermal displacements of the atoms. This is achieved by modelling the total scattered intensity, comprising both the Bragg peak intensity and the diffuse contribution to the spectrum from the scattering density of an assembly of atoms. This assembly, which is constructed by the reverse Monte Carlo technique so as to yield a best fit with the data, is then used to calculate the average atomic thermal displacements. This allows for a refinement that, in principle, no longer requires angle-dependent background parameters, and that is well suited for dealing with highly anisotropic Debye–Waller factors and split atomic sites.

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