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Data Analysis papers — page 140

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A Unified Approach to the Classical Statistical Analysis of Small Signals
We give a classical confidence belt construction which unifies the treatment of upper confidence limits for null results and two-sided confidence intervals for non-null results. The unified treatment…
Gary J. Feldman, Robert D. CousinsNov 22, 1997
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Aftershocks in Coherent-Noise Models
The decay pattern of aftershocks in the so-called 'coherent-noise' models [M. E. J. Newman and K. Sneppen, Phys. Rev. E54, 6226 (1996)] is studied in detail. Analytical and numerical results…
C. Wilke, S. Altmeyer, T. MartinetzOct 20, 1997
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BAYES-LIN: An object-oriented environment for Bayes linear local computation
BAYES-LIN is an extension of the LISP-STAT object-oriented statistical computing environment, which adds to LISP-STAT some object prototypes appropriate for carrying out local computation via…
Darren J WilkinsonOct 16, 1997
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Incorporation of the statistical uncertainty in the background estimate into the upper limit on the signal
We present a procedure for calculating an upper limit on the number of signal events which incorporates the Poisson uncertainty in the background, estimated from control regions of one or two…
K. K. GanJul 25, 1997
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Experiments on Critical Phenomena in a Noisy Exit Problem
We consider noise-driven exit from a domain of attraction in a two-dimensional bistable system lacking detailed balance. Through analog and digital stochastic simulations, we find a theoretically…
D. G. Luchinsky, R. S. Maier, R. Mannella et al.Jul 1, 1997
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Objective Bayesian Statistics
Bayesian inference --- although becoming popular in physics and chemistry --- is hampered up to now by the vagueness of its notion of prior probability. Some of its supporters argue that this…
O. -A. Al-Hujaj, H. L. HarneyJun 18, 1997
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The Analysis of Data from Continuous Probability Distributions
Conventional statistics begins with a model, and assigns a likelihood of obtaining any particular set of data. The opposite approach, beginning with the data and assigning a likelihood to any…
Timothy E. HolyJun 10, 1997
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Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean…
Radford M. NealJan 28, 1997
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A Theory of Measurement Uncertainty Based on Conditional Probability
A theory of measurement uncertainty is presented, which, since it is based exclusively on the Bayesian approach and on the subjective concept of conditional probability, is applicable in the most…
G. D'AgostiniNov 21, 1996
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