Results 251 to 260 of about 4,423,036 (299)
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1994
In linear regression the mean surface in sample space is a plane. In non-linear regression the mean surface may be an arbitrary curved surface but in other respects the models are similar. In practice the mean surface in most non-linear regression models will be approximately planar in the region(s) of high likelihood allowing good approximations based
W. N. Venables, B. D. Ripley
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In linear regression the mean surface in sample space is a plane. In non-linear regression the mean surface may be an arbitrary curved surface but in other respects the models are similar. In practice the mean surface in most non-linear regression models will be approximately planar in the region(s) of high likelihood allowing good approximations based
W. N. Venables, B. D. Ripley
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Exploration Geophysics, 1977
A non-linear optimisation procedure has been applied to the interpretation of two-dimensional gravity anomalies. In comparison with linear methods, the non-linear approach is more difficult to control but can give more realistic solutions. Successful application of the non-linear approach depends upon the selection of a reasonable starting model from ...
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A non-linear optimisation procedure has been applied to the interpretation of two-dimensional gravity anomalies. In comparison with linear methods, the non-linear approach is more difficult to control but can give more realistic solutions. Successful application of the non-linear approach depends upon the selection of a reasonable starting model from ...
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Bayesian Models for Non‐linear Autoregressions
Journal of Time Series Analysis, 1997We discuss classes of Bayesian mixture models for nonlinear autoregressive times series, based on developments in semiparametric Bayesian density estimation in recent years. The development involves formal classes of multivariate discrete mixture distributions, providing flexibility in modeling arbitrary nonlinearities in time series structure and a ...
Müller, Peter +2 more
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1989
In previous chapters we have encountered several models which depend on parameters that introduce parameter non-linearities into otherwise standard DLMs. Although the full class of DLMs provides an enormous variety of useful models, it is the case that, sometimes, elaborations to include models with unknown parameters result in such non-linearities ...
Mike West, Jeff Harrison
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In previous chapters we have encountered several models which depend on parameters that introduce parameter non-linearities into otherwise standard DLMs. Although the full class of DLMs provides an enormous variety of useful models, it is the case that, sometimes, elaborations to include models with unknown parameters result in such non-linearities ...
Mike West, Jeff Harrison
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ACE: A non-linear regression model
Chemometrics and Intelligent Laboratory Systems, 1988Abstract Frank, I.E. and Lanteri, S., 1988. ACE: a non-linear regressional model. Chemometrics and Intelligent Laboratory Systems , 3: 301–313. A non-linear regression model is discussed and applied to various structure—activity relationship problems.
I. E. FRANK, LANTERI, SILVIA
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OPTIMAL CONTROL OF STOCHASTIC NON-LINEAR MODELS
IFAC Proceedings Volumes, 1989The use of large macroeconometric models has become central to the policy work of many national and international agencies. The models are not only complex in terms of their size but also invariably non-linear. Using such a model in policy formulation inevitably leads towards specifying a set of objectives and trying to meet these objectives as closely
S. G. Hall, M. J. Stephenson
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Non-linear compartmental models
Advances in Applied Probability, 1984The linear compartmental model arises when ‘particles’ move independently between (or out of) a system of compartments in a stochastically similar way. With a given ‘initial’ particle count, the subsequent compartmental particle counts follow multinomial probability distributions (Faddy (1976)) for Markov or semi-Markov movement processes.
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Application of non‐linear elastic model
International Journal for Numerical and Analytical Methods in Geomechanics, 1992AbstractThe properties of a previously published isotropic non‐linear elastic model with an elastic potential are illustrated in detail. Its parameter determination is elaborated and a parameter list is composed on the basis of published experimental data. The formulation with the elastic potential is shown to enable the minimization of numerical drift
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1997
Further models of two interacting quantities are studied in this chapter. These models are distinguished from those of the previous chapter because they lead to coupled non-linear, rather than linear, differential equations. Such equations usually cannot be solved explicitly.
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Further models of two interacting quantities are studied in this chapter. These models are distinguished from those of the previous chapter because they lead to coupled non-linear, rather than linear, differential equations. Such equations usually cannot be solved explicitly.
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