M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. SpringerVerlag, 2nd edition, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Unfortunately, in many cases such explicit error models do not exist since it is impossible to predict all errors a user might make. Another common approach in dynamic systems is to monitor the residuals of observations, thereby testing the appropriateness of the underlying model assumptions [11, 166]. We propose an alternative, more capable approach to overcome the limitations of these methods. Our technique is based on online model selection, which aims at identifying the model that is best suited to explain the observed data [166, 125] The quality of a model is given by its predictive ....

....the appropriateness of the underlying model assumptions [11, 166] We propose an alternative, more capable approach to overcome the limitations of these methods. Our technique is based on online model selection, which aims at identifying the model that is best suited to explain the observed data [166, 125]. The quality of a model is given by its predictive performance, i.e. the likelihood of the observed data given the model. Multiple models can be compared using Bayes factors [55, 166] which then yield the ratio of posterior model probabilities. To apply model selection in our context, we will ....

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M. West and P.J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York, 2nd edition, 1997.

....from the instantiations of a class are considered (virtual) cases of that class. 39] give both theoretical as well as empirical evidence that this learning method is superior to Note that this approach can be seen as a generalization of the method for parameter learning in DBNs, see e.g. [57]. conventional parameter learning in object oriented domains. 5.1 Structural OO learning The goal of our learning algorithm is to nd a good estimate of the unknown underlying statistical distribution function, i.e. the task of density estimation [52] Note that if focus had been on e.g. ....

Mike West and Je Harrison. Bayesian Forecasting and Dynamic Models. Springer Verlag, New York, 2nd edition, 1997.

....m1 . Merging the two components of this submixture is equivalent to finding the parameters of the closest Gaussian density. If closeness is taken in the KL sense, then # C ) arg min #C D (yj m1 # Cm1 ) ff (yj m1 # Cm1 )# (yj# C) which has a simple solution (see [34], Chapp. 12) are the global mean and covariance of the given two component mixture, i.e. m1 m1 ff m2 m2 (18) ff ) 19) This means that when merging components m 1 and m 2 of the mixture, the resulting component must retain the combined probability, mean, and ....

M. West and J Harrison. Bayesian Forecasting and Dynamic Models. SpringerVerlag, New York, 1989.

....the theory of the general dynamic linear model is presented. Mainly, we will be engaged with definitions and the Kalman filter. At the end of this chapter the dynamic linear model is applied to the pigs data. The theory of this chapter is based on [Gammelgaard et al. 1995, Chapter 2] and [West and Harrison, 1989, Chapter 2, 3, and 4] 7.1 Bayesian learning and dynamic linear models A statistical analysis of a set of data principally comprises of making inference about the underlying structure described by the distribution from which the data were generated. If this distribution is assumed known in ....

....a weighted expression of the one step forecast variance matrix Q t , i.e. the expected variance matrix of the observation Y t . For further elaborations of the adaption coefficient A t in the special case of the Kalman filter applied to a simple time series DLM see [Gammelgaard et al. 1995] and [West and Harrison, 1989] . In this section, a DLM will be applied to model the pigs data. We will confine ourselves to model the weight of a single pig, as this immediately can be extended to model the weights of a class of pigs, e.g. the complete set of data or a single breed. First, the DLM used in the following will ....

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M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1989.

....and models of reasoning. In some domains, the detection of relevant events depends on analyzing past as well as present data; methodologies that reason explicitly about time include trend detection [1, 15] knowledgebased interval abstraction [47] and time series analysis and forecasting [6, 53]. These temporal reasoning methodologies may be used to warn of impending events, as well as transpiring events. Alarms that utilize the above inference methodologies are often described as intelligent sometimes because of the complexity of inferences, sometimes because the inference mechanism ....

Mike West and Jeff Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York, 1989.

....a time series of observations, and then updating that model as new observations arrive. Probability forecasting methods, in particular, can reason about uncertain system states and uncertain future effects due to unmodeled exogenous influences. Despite recent advances in probability forecasting [12, 14, 66], however, they are largely limited to predicting future states assuming no interventions. Thus, a PCM must integrate desirable features that are found in mathematical models of dynamical systems and forecasting models. If we represent actions with a time dependent control variable # t and system ....

....prediction task: 1) transfer function models, and 2) delay coordinate embedding (DCE) models. Transfer function models, which are rooted in engineering mathematics, are stochastic linear difference equations that can be used to predict intervention effects for a broad class of dynamic processes [29, 66]. DCE models, which are grounded in theoretical results from mathematical physics [61] can be used to make similar predictions for complex nonlinear dynamical systems with nonnormal uncertainties [15] We will explore and optimize automatic methods for constructing these models from time series ....

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Mike West and Jeff Harrison. Bayesian Forecasting and Dynamic Models. SpringerVerlag, New York, 1989.

....this submixture is equivalent to finding the parameters and C of the closest Gaussian density. If closeness is taken in the KL sense, then ( C ) arg min ;C D Theta ff 0 m1 N (yj m1 ; Cm1 ) ff 0 m2 N (yj m1 ; Cm1 ) N (yj; C) which has a simple solution (see [34], Chapp. 12) and C are the global mean and covariance of the given two component mixture, i.e. ff 0 m1 m1 ff 0 m2 m2 (18) C = ff 0 m1 (Cm1 m1 T m1 ) ff 0 m2 (Cm2 m2 T m2 ) Gamma T : 19) This means that when merging components m 1 and m ....

M. West and J Harrison. Bayesian Forecasting and Dynamic Models. SpringerVerlag, New York, 1989.

....we are interested in developing algorithms to process data arriving on line. This is a complex optimal nonlinear ltering problem. Many approximation algorithms, such as the extended Kalman lter and Gaussian sum approximations, have been proposed to surmount this problem (Anderson and Moore 1979, West and Harrison 1997). However, in many realistic problems, these approximating methods are notoriously unreliable and faults are dicult to diagnose on line. Recently there has been a surge of interest in sequential Monte Carlo (SMC) methods (also known as particle ltering when the objective of the analysis is to ....

West, M. and Harrison, J. (1997). Bayesian forecasting and dynamic models, Springer Series in Statistics, second edn, Springer-Verlag, New York.

....time series analysis which can be found in the literature. In an early attempt to apply statistical time series analysis to online monitoring data, Smith and West [13] used a multiprocess dynamic linear model to monitor patients after renal transplantation. In dynamic linear models (DLMs) [58] the observation X t at time t is regarded as a linear transform of an unobservable state parameter. This state is assumed to change dynamically in time according to a simple regression model. Particularly, the linear growth model X t = t # t t = t 1 # t 1 # t,1 # t = # t 1 # t,2 is ....

West, M., and Harrison, J. (1989), Bayesian Forecasting and Dynamic Models, Springer, New York.

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M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. SpringerVerlag, 2nd edition, 1997.

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M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York, 1997.

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West M. and Harrison J.F. (1997) Bayesian Forecasting and Dynamic Models, Springer Verlag Series in Statistics, 2 nd edition.

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West, M. and Harrison, J. (1997). Bayesian forecasting and dynamic models, Springer Series in Statistics, second edn, Springer-Verlag, New York.

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West, M. and Harrison, P. J. (1997). Bayesian Forecasting and Dynamic Models, 2 nd ed., Springer-Verlag, New York.

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M. West and J. Harrison. Bayesian forecasting and dynamic models. Springer-Verlag, 1997. 42

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Mike West and Jeff Harrison. Bayesian forecasting and dynamic models. Springer, 1997. 15

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West, M. and Harrison, P.J. (1997) Bayesian Forecasting and Dynamic Models, 2nd edn. Springer-Verlag, New York.

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West, M. and Harrison, P.J. (1997) Bayesian Forecasting and Dynamic Models. 2nd edition. Springer Verlag, New York.

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M. West and J. Harrison, Bayesian forecasting and dynamic models, Springer, 1999.

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M. West and P. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1989.

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M. West and P.J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York, 2nd edition, 1997.

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M. West and P.J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York, 2nd edition, 1997.

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M. West and P. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1989.

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West M. and Harrison P.J. (1997) Bayesian Forecasting and Dynamic Models. 2nd edition, New York: Springer-Verlag.

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West, Mike, and Je# Harrison. 1997. Bayesian Forecasting and Dynamic Models . New York: Springer.

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