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Model Class (model + class)
Selected AbstractsCalculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System DataCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2010Sai Hung Cheung Because of modeling uncertainty, a set of competing candidate model classes may be available to represent a system and it is then desirable to assess the plausibility of each model class based on system data. Bayesian model class assessment may then be used, which is based on the posterior probability of the different candidates for representing the system. If more than one model class has significant posterior probability, then Bayesian model class averaging provides a coherent mechanism to incorporate all of these model classes in making probabilistic predictions for the system response. This Bayesian model assessment and averaging requires calculation of the evidence for each model class based on the system data, which requires the evaluation of a multi-dimensional integral involving the product of the likelihood and prior defined by the model class. In this article, a general method for calculating the evidence is proposed based on using posterior samples from any Markov Chain Monte Carlo algorithm. The effectiveness of the proposed method is illustrated by Bayesian model updating and assessment using simulated earthquake data from a ten-story nonclassically damped building responding linearly and a four-story building responding inelastically. [source] Predicting the past distribution of species climatic nichesGLOBAL ECOLOGY, Issue 5 2009David Nogués-Bravo ABSTRACT Predicting past distributions of species climatic niches, hindcasting, by using climate envelope models (CEMs) is emerging as an exciting research area. CEMs are used to examine veiled evolutionary questions about extinctions, locations of past refugia and migration pathways, or to propose hypotheses concerning the past population structure of species in phylogeographical studies. CEMs are sensitive to theoretical assumptions, to model classes and to projections in non-analogous climates, among other issues. Studies hindcasting the climatic niches of species often make reference to these limitations. However, to obtain strong scientific inferences, we must not only be aware of these potential limitations but we must also overcome them. Here, I review the literature on hindcasting CEMs. I discuss the theoretical assumptions behind niche modelling, i.e. the stability of climatic niches through time and the equilibrium of species with climate. I also summarize a set of ,recommended practices' to improve hindcasting. The studies reviewed: (1) rarely test the theoretical assumptions behind niche modelling such as the stability of species climatic niches through time and the equilibrium of species with climate; (2) they only use one model class (72% of the studies) and one palaeoclimatic reconstruction (62.5%) to calibrate their models; (3) they do not check for the occurrence of non-analogous climates (97%); and (4) they do not use independent data to validate the models (72%). Ignoring the theoretical assumptions behind niche modelling and using inadequate methods for hindcasting CEMs may well entail a cascade of errors and naïve ecological and evolutionary inferences. We should also push integrative research lines linking macroecology, physiology, population biology, palaeontology, evolutionary biology and CEMs for a better understanding of niche dynamics across space and time. [source] Identification of a class of non-linear parametrically varying modelsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2003F. Previdi The aim of this paper is to propose a novel class of non-linear, possibly parameter-varying models suitable for system identification purposes. These models are given in the form of a linear fractional transformation (LFT) where the ,forward' part is represented by a conventional linear regression and the ,feedback' part is given by a non-linear dynamic map parameterized by a neural network (NN) which can take into account scheduling variables available for measurement. For this specific model structure a parameter estimation procedure has been set up, which turns out to be particularly efficient from the computational point of view. Also, it is possible to establish a connection between this model class and the well known class of local model networks (LMNs): this aspect is investigated in the paper. Finally, we have applied the proposed identification procedure to the problem of determining accurate non-linear models for knee joint dynamics in paraplegic patients, within the framework of a functional electrical stimulation (FES) rehabilitation engineering project. Copyright © 2002 John Wiley & Sons, Ltd. [source] Forecasting high-frequency financial data with the ARFIMA,ARCH modelJOURNAL OF FORECASTING, Issue 7 2001Michael A. Hauser Abstract Financial data series are often described as exhibiting two non-standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA,ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid (1987). For daily data on the Swiss 1-month Euromarket interest rate during the period 1986,1989, the ARFIMA,ARCH (5,d,2/4) model with non-integer d is selected by AIC. Model-based out-of-sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (, > 40). Regarding volatility forecasts, however, the selected ARFIMA,ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd. [source] Binary models for marginal independenceJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2008Mathias Drton Summary., Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach that is taken is graphical and draws on analogies with multivariate Gaussian models for marginal independence. For the graphical model representation we use bidirected graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed by using a version of the iterated conditional fitting algorithm. Finally we consider combining these models with symmetry restrictions. [source] A note on penalized minimum distance estimation in nonparametric regressionTHE CANADIAN JOURNAL OF STATISTICS, Issue 3 2003Florentina Bunea Abstract The authors introduce a penalized minimum distance regression estimator. They show the estimator to balance, among a sequence of nested models of increasing complexity, the L1 -approximation error of each model class and a penalty term which reflects the richness of each model and serves as a upper bound for the estimation error. Les auteurs présentent un nouvel estimateur de régression obtenu par minimisation d'une distance pénalisée. Ils montrent que pour une suite de modèles embo,tés à complexité croissante, cet estimateur offre un bon compromis entre l'erreur d'approximation L1 de chaque classe de modèles et un terme de pénalisation permettant à la fois de refléter la richesse de chaque modèle et de majorer l'erreur d'estimation. [source] Calculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System DataCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2010Sai Hung Cheung Because of modeling uncertainty, a set of competing candidate model classes may be available to represent a system and it is then desirable to assess the plausibility of each model class based on system data. Bayesian model class assessment may then be used, which is based on the posterior probability of the different candidates for representing the system. If more than one model class has significant posterior probability, then Bayesian model class averaging provides a coherent mechanism to incorporate all of these model classes in making probabilistic predictions for the system response. This Bayesian model assessment and averaging requires calculation of the evidence for each model class based on the system data, which requires the evaluation of a multi-dimensional integral involving the product of the likelihood and prior defined by the model class. In this article, a general method for calculating the evidence is proposed based on using posterior samples from any Markov Chain Monte Carlo algorithm. The effectiveness of the proposed method is illustrated by Bayesian model updating and assessment using simulated earthquake data from a ten-story nonclassically damped building responding linearly and a four-story building responding inelastically. [source] Principles and applications of control in quantum systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 15 2005Hideo Mabuchi Abstract We describe in this article some key themes that emerged during a Caltech/AFOSR Workshop on ,Principles and Applications of Control in Quantum Systems' (PRACQSYS), held 21,24 August 2004 at the California Institute of Technology. This workshop brought together engineers, physicists and applied mathematicians to construct an overview of new challenges that arise when applying constitutive methods of control theory to nanoscale systems whose behaviour is manifestly quantum. Its primary conclusions were that the number of experimentally accessible quantum control systems is steadily growing (with a variety of motivating applications), that appropriate formal perspectives enable straightforward application of the essential ideas of classical control to quantum systems, and that quantum control motivates extensive study of model classes that have previously received scant consideration. Copyright © 2005 John Wiley & Sons, Ltd. [source] | |||||||||||||