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Results #1-#10 of 19
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1.
Teaching statistics in the physics curriculum: Unifying and clarifying role of subjective probability
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100
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G. D'Agostini, Am. J. Phys.,
67
(12), 9 (1999).
The author introduces the ideas of subjective probability and Bayesian inference, comments on typical misconceptions that tend to discredit it, and compares it to other approaches.
https://doi.org/10.1119/1.19113
2.
Random multiplicative processes: An elementary tutorial
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100
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S. Redner, Am. J. Phys.,
58
(3), 267-273 (1990).
An elementary discussion of the statistical properties of the product of N independent random variables is given. The motivation is to emphasize the essential differences between the…
https://doi.org/10.1119/1.16497
3.
Review of Statistical and Thermal Physics with Computer Applications
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100
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D. Lemons, Am. J. Phys.,
79
(4), 431-432 (2011).
An review by Don S. Lemons in the American Journal of Physics of the textbook by Gould and Tobochnik.
https://doi.org/10.1119/1.3545813
4.
Book Review by Daniel ben-Avraham of Statistical and Thermal Physics with Computer Applications by Harvey Gould and Jan Tobochnik
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100
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D. ben-Avraham, J. Stat. Phys.,
140
(5), 1022-1023 (2010).
An review by Daniel ben-Avraham in the Journal of Statistical Physics of the textbook by Gould and Tobochnik.
https://doi.org/10.1007/s10955-010-0035-4
5.
Watching a drunkard for 10 nights: A study of distributions of variances
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R. Zia and B. Schmittmann, Am. J. Phys.,
71
(9), 859-875 (2003).
The most frequently studied quantities of a statistical variable are its average and standard deviation. Yet, its full distribution often carries very interesting information and can…
https://doi.org/10.1119/1.1566430
6.
Stirling’s formula!
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N. Mermin, Am. J. Phys.,
52
(4), 362-365 (1983).
Stirling’s approximation to n! and other estimates are developed using elementary arguments. The aim is to shed light on why these approximations work so well. An elementary and…
https://doi.org/10.1119/1.13670
7.
A new approach to Monte Carlo simulations in statistical physics: Wang-Landau sampling
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D. Landau, S. Tsai, and M. Exler, Am. J. Phys.,
72
(10), 1294-1302 (2004).
The authors describe a Monte Carlo algorithm for sampling the density of states directly by doing a random walk in energy space. The probability of a microstate is computed at any…
https://doi.org/10.1119/1.1707017
8.
The critical temperature of two-dimensional and three-dimensional Ising models
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B. Liu and M. Gitterman, Am. J. Phys.,
71
(8), 806-808 (2003).
The critical temperatures of the Ising model is obtained by considering the elementary cells of the square lattice in two dimensions and the cubic lattice in three dimensions. The…
https://doi.org/10.1119/1.1579495
9.
A walk in phase space: Solidification into crystalline and amorphous states
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J. Adler, Am. J. Phys.,
67
(12), 1145-1148 (1999).
The formation of crystalline and amorphous solids is described using a simple glass bead demonstration and a discussion of annealing and rapid quenching on a computer.
https://doi.org/10.1119/1.19099
10.
Percolation clusters as teaching aid for Monte Carlo simulation and critical exponents
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100
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D. Stauffer, Am. J. Phys.,
45
(10), 1001-1002 (1977).
The author discusses a geometrical phase transition involving percolation clusters. This continuous phase transition can be easily simulated on a computer and is analogous to the…
https://doi.org/10.1119/1.10872
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Results #1-#10 of 19
