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Statistical models and thermalization
Nuclear Physics B - Proceedings Supplements, 2003Abstract The status of thermodynamical is discussed. This approach is quiet popular in the heavy ion collision physics. It is argued that the “principle of vanishing of correlations” must be used for quantitative estimations of the rate of thermalization.
A. Sissakian, J. Manjavidze
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Statistical models and statistical inference
1981In the previous chapter we have seen how a simple statistical model can be fitted to data by estimating the unknown parameters and then making checks with residuals. After we have done this, various questions can be answered in terms of the fitted model.
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Statistical manifolds are statistical models
Journal of Geometry, 2006In this note we prove that any smooth (C1 resp.) statistical manifold can be embedded into the space of probability measures on a finite set. As a result, we get positive answers to Lauritzen’s question and Amari’s question on a realization of smooth (C1 resp.) statistical manifolds as finite dimensional statistical models.
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A statistical model in palynology
Geoscience and Man, 1971Abstract The causes of areal and temporal variation in palynofloras extracted from sedimentary rocks are divided into major sources of variation and extraneous sources of variation. Major sources include: (1) Time; (2) Climate; (3) Plant succession; (4) Variations in local weather conditions; (5) Area dominated by species; (6) Distance transported; (7)
Raymond A. Christopher, George F. Hart
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Statistical Models for Fracture
1998Recent developments in statistical physics studying fracture phenomena are reviewed. A quantity of experimental interest is the breaking characteristics of the system (force vs. displacement): we discuss its universal scaling behaviour. Moreover, the distribution of local strain has multifractal scaling properties just before the system breaks fully ...
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Statistics for Model Calibration
2015Mathematical models of dynamic processes contain parameters which have to be estimated based on time-resolved experimental data. This task is often approached by optimization of a suitably chosen objective function. Maximization of the likelihood, i.e.
Kreutz, Clemens +2 more
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2020
This chapter covers some of the basics of time series statistics as they relate to our model. An analytic solution to our model is derived with log preferences. Then the chapter discusses how to program up the model and compute sample statistic from model simulations.
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This chapter covers some of the basics of time series statistics as they relate to our model. An analytic solution to our model is derived with log preferences. Then the chapter discusses how to program up the model and compute sample statistic from model simulations.
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Statistical Modeling for Management
2008Measurement Scales Modeling Continuous Data Modeling Dichotomous Data Modeling Ordered Data Modeling Unordered Data Neural Networks Approximate Algorithms for Management Problems Other Statistical, Mathematical and Co-pattern Modeling ...
Hutcheson, G., Moutinho, L.
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2018
“Why do I need to take statistics?” Every semester that I teach statistics I have students asking this question. It's a fair question to ask. Most of my students want to work as therapists, social workers, psychologists, anthropologists, sociologists, teachers, or in other jobs that seem far removed from formulas and numbers.
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“Why do I need to take statistics?” Every semester that I teach statistics I have students asking this question. It's a fair question to ask. Most of my students want to work as therapists, social workers, psychologists, anthropologists, sociologists, teachers, or in other jobs that seem far removed from formulas and numbers.
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1984
We adopt the following formulation of the structural form of the dynamic linear simultaneous equation model: $$ XA = \tilde YB + Z\Gamma = \tilde U $$ (1.1)
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We adopt the following formulation of the structural form of the dynamic linear simultaneous equation model: $$ XA = \tilde YB + Z\Gamma = \tilde U $$ (1.1)
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