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Model Parameter Estimates (model + parameter_estimate)
Selected AbstractsThermal performance of juvenile Atlantic Salmon, Salmo salar L.FUNCTIONAL ECOLOGY, Issue 6 2001B. JONSSON Summary 1,Experimental data for maximum growth and food consumption of Atlantic Salmon (Salmo salar L.) parr from five Norwegian rivers situated between 59 and 70°N were analysed and modelled. The growth and feeding models were also applied to groups of Atlantic Salmon growing and feeding at rates below the maximum. The data were fitted to the Ratkowsky model, originally developed for bacterial growth. 2,The rates of growth and food consumption varied significantly among populations but the variation appeared unrelated to thermal conditions in the river of population origins. No correlation was found between the thermal conditions and limits for growth, thermal growth optima or maximum growth, and hypotheses of population-specific thermal adaptation were not supported. Estimated optimum temperatures for growth were between 16 and 20 °C. 3, Model parameter estimates differed among growth-groups in that maximum growth and the performance breadth decreased from fast to slow growing individuals. The optimum temperature for growth did not change with growth rate. 4, The model for food consumption (expressed in energy terms) peaked at 19,21 °C, which is only slightly higher than the optimal temperature for growth. Growth appeared directly related to food consumption. Consumption was initiated ,2 °C below the lower temperature for growth and terminated ,1·5 °C above the upper critical temperature for growth. Model parameter estimates for consumption differed among growth-groups in a manner similar to the growth models. 5,By combining the growth and consumption models, growth efficiencies were estimated. The maximum efficiencies were high, 42,58%, and higher in rivers offering hostile than benign feeding and growth opportunities. [source] Artificial neural networks for parameter estimation in geophysicsGEOPHYSICAL PROSPECTING, Issue 1 2000Carlos Calderón-Macías Artificial neural systems have been used in a variety of problems in the fields of science and engineering. Here we describe a study of the applicability of neural networks to solving some geophysical inverse problems. In particular, we study the problem of obtaining formation resistivities and layer thicknesses from vertical electrical sounding (VES) data and that of obtaining 1D velocity models from seismic waveform data. We use a two-layer feedforward neural network (FNN) that is trained to predict earth models from measured data. Part of the interest in using FNNs for geophysical inversion is that they are adaptive systems that perform a non-linear mapping between two sets of data from a given domain. In both of our applications, we train FNNs using synthetic data as input to the networks and a layer parametrization of the models as the network output. The earth models used for network training are drawn from an ensemble of random models within some prespecified parameter limits. For network training we use the back-propagation algorithm and a hybrid back-propagation,simulated-annealing method for the VES and seismic inverse problems, respectively. Other fundamental issues for obtaining accurate model parameter estimates using trained FNNs are the size of the training data, the network configuration, the description of the data and the model parametrization. Our simulations indicate that FNNs, if adequately trained, produce reasonably accurate earth models when observed data are input to the FNNs. [source] Influences of information processing and disengagement in infants' looking behaviourINFANT AND CHILD DEVELOPMENT, Issue 2 2010Holger Domsch Abstract The present study considers the joint influences of information processing and disengagement in looking behaviour within a habituation paradigm. Six-month-old infants were habituated, during which their heart rate (HR) was measured. A parametric model of habituation yielded for each infant parameter estimates of their habituation performance. These parameters were interpreted as assessing information processing and disengagement. Corresponding measures were obtained from the HR data. The HR measures and habituation model parameter estimates were significantly correlated, as predicted. In addition, an attention getter, presented prior to each habituation trial, influenced indicators of information processing, but not of disengagement. Results confirmed the advantages of a modelling approach. In addition, and more importantly, findings led to the conclusion that both information processing as well as disengagement are involved in infants' looking behaviour in visual habituation. Copyright © 2009 John Wiley & Sons, Ltd. [source] The equilibrium assumption in estimating the parameters of metapopulation modelsJOURNAL OF ANIMAL ECOLOGY, Issue 1 2000Atte Moilanen 1.,The construction of a predictive metapopulation model includes three steps: the choice of factors affecting metapopulation dynamics, the choice of model structure, and finally parameter estimation and model testing. 2.,Unless the assumption is made that the metapopulation is at stochastic quasi-equilibrium and unless the method of parameter estimation of model parameters uses that assumption, estimates from a limited amount of data will usually predict a trend in metapopulation size. 3.,This implicit estimation of a trend occurs because extinction-colonization stochasticity, possibly amplified by regional stochasticity, leads to unequal numbers of observed extinction and colonization events during a short study period. 4.,Metapopulation models, such as those based on the logistic regression model, that rely on observed population turnover events in parameter estimation are sensitive to the implicit estimation of a trend. 5.,A new parameter estimation method, based on Monte Carlo inference for statistically implicit models, allows an explicit decision about whether metapopulation quasi-stability is assumed or not. 6.,Our confidence in metapopulation model parameter estimates that have been produced from only a few years of data is decreased by the need to know before parameter estimation whether the metapopulation is in quasi-stable state or not. 7.,The choice of whether metapopulation stability is assumed or not in parameter estimation should be done consciously. Typical data sets cover only a few years and rarely allow a statistical test of a possible trend. While making the decision about stability one should consider any information about the landscape history and species and metapopulation characteristics. [source] | ||||||||||