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A state-space modeling approach for active structural acoustic control

OLIVEIRA, Leopoldo P. R. de; VAROTO, Paulo S.; SAS, Paul; DESMET, Wim
Fonte: IOS PRESS Publicador: IOS PRESS
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.7%
The demands for improvement in sound quality and reduction of noise generated by vehicles are constantly increasing, as well as the penalties for space and weight of the control solutions. A promising approach to cope with this challenge is the use of active structural-acoustic control. Usually, the low frequency noise is transmitted into the vehicle`s cabin through structural paths, which raises the necessity of dealing with vibro-acoustic models. This kind of models should allow the inclusion of sensors and actuators models, if accurate performance indexes are to be accessed. The challenge thus resides in deriving reasonable sized models that integrate structural, acoustic, electrical components and the controller algorithm. The advantages of adequate active control simulation strategies relies on the cost and time reduction in the development phase. Therefore, the aim of this paper is to present a methodology for simulating vibro-acoustic systems including this coupled model in a closed loop control simulation framework that also takes into account the interaction between the system and the control sensors/actuators. It is shown that neglecting the sensor/actuator dynamics can lead to inaccurate performance predictions.; KU Leuven; University of Sao Paulo (USP); European FP6 Integrated Project

Digital filtering of oscillations intrinsic to transmission line modeling based on lumped parameters

Da Costa, Eduardo Coelho Marques; Kurokawa, Sérgio; Shinoda, Ailton Akira; Pissolato, José
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Artigo de Revista Científica Formato: 908-915
ENG
Relevância na Pesquisa
55.84%
A correction procedure based on digital signal processing theory is proposed to smooth the numeric oscillations in electromagnetic transient simulation results from transmission line modeling based on an equivalent representation by lumped parameters. The proposed improvement to this well-known line representation is carried out with an Finite Impulse Response (FIR) digital filter used to exclude the high-frequency components associated with the spurious numeric oscillations. To prove the efficacy of this correction method, a well-established frequency-dependent line representation using state equations is modeled with an FIR filter included in the model. The results obtained from the state-space model with and without the FIR filtering are compared with the results simulated by a line model based on distributed parameters and inverse transforms. Finally, the line model integrated with the FIR filtering is also tested and validated based on simulations that include nonlinear and time-variable elements. © 2012 Elsevier Ltd. All rights reserved.

Modelagem computacional de dados e controle inteligente no espaço de estado; State space computational data modelling and intelligent control

Annabell Del Real Tamariz
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 15/07/2005 PT
Relevância na Pesquisa
65.73%
Este estudo apresenta contribuições para modelagem computacional de dados multivariáveis no espaço de estado, tanto com sistemas lineares invariantes como com variantes no tempo. Propomos para modelagem determinística-estocástica de dados ruidosos, o Algoritmo MOESP_AOKI. Propomos, utilizando Redes Neurais Recorrentes multicamadas, algoritmos para resolver a Equação Algébrica de Riccati Discreta bem como a Inequação Algébrica de Riccati Discreta, via Desigualdades Matriciais Lineares. Propomos um esquema de controle adaptativo com Escalonamento de Ganhos, baseado em Redes Neurais, para sistemas multivariáveis discretos variantes no tempo, identificados pelo algoritmo MOESP_VAR, também proposto nesta tese. Em síntese, uma estrutura de controle inteligente para sistemas discretos multivariáveis variantes no tempo, através de uma abordagem que pode ser chamada ILPV (Intelligent Linear Parameter Varying), é proposta e implementada. Um controlador LPV Inteligente, para dados computacionalmente modelados pelo algoritmo MOESP_VAR, é concretizado, implementado e testado com bons resultados.; This study presents contributions for state space multivariable computational data modelling with discrete time invariant as well as with time varying linear systems. A proposal for Deterministic-Estocastica Modelling of noisy data...

Modelagem computacional de válvula de expansão eletrônica para sistema de refrigeração e ar condicionado; Computational modeling of electronic expansion valve in a refrigeration system and air conditioning

Ana Maria Ramirez Buitrago
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 22/02/2010 PT
Relevância na Pesquisa
55.9%
Neste trabalho apresentamos a modelagem computacional de uma válvula de expansão eletrônica a partir de dados experimentais de entradas e saídas através de modelos no espaço de estado, usando técnicas de subespaços, com objetivo de ter um sistema de refrigeração e ar condicionado eficiente, combinando eletrônica de potência e computação de modo a fornecer uma melhor solução para conservação de energia. A modelagem e a validação são feitas usando uma implementação computacional dos algoritmos de subespaços do espaço de estado. Os resultados apresentados mostram a validade e vantagens da técnica de modelagem realizada; This research shows the computational modeling of a electronic expansion valve based on input and output experimental data using Models in State Space and subspace methods. The aim of this work was to obtain an efficient Cooling and Air Conditioning system by the combination of power electronics and computation, as a result, a better solution for energy conservation was obtained. Modeling and validation are made using a computational implementation of subspace methods algorithms in state space. Achieved results show the validity and advantages of the modeling technique implemented

Proposta de uma metodologia aprimorada para modelagem de linhas de transmissão no espaço de estados; Proposal of an enhanced methodology for transmission lines modeling in the space state

Costa, Eduardo Coelho Marques da
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 18/01/2013 PT
Relevância na Pesquisa
55.97%
Uma metodologia alternativa e aprimorada para modelagem de linhas de transmissão multifásicas é apresentada ao longo do desenvolvimento proposto. O desacoplamento modal das fases e cabos pára-raios dá-se por meio de uma metodologia otimizada no uso das matrizes de transformação modal ao longo das sucessivas transformações entre os domínios dos modos e das fases, eliminando os erros decorrentes da modelagem e representação da linha fazendo uso de análise modal. A representação equivalente de cada modo de propagação é desenvolvida por elementos discretos convencionais com base na teoria fundamental de circuitos elétricos, o que torna a modelagem em questão simplificada, porém não menos precisa. Para modelagem do efeito da frequência nos parâmetros longitudinais da linha, é utilizado vector fitting para sintetizar os parâmetros de forma equivalente e por elementos discretos para cada modo de propagação do sistema multifásico. O sistema de equações diferenciais é representado no espaço de estados e facilmente solucionado por métodos numéricos de integração. No entanto, propõe-se a resolução do sistema de equações de estado por meio de um método de solução analítico, significativamente mais eficaz computacionalmente e mais robusto que o método de integração trapezoidal...

Propostas para modelagem computacional de series temporais e de sistemas multivariaveis variantes no tempo no espaço de estado; Proposals for computer modelling of multivariable non-stationary time series and systems in the state space

Johanna Belen Tobar Quevedo
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 04/04/2013 PT
Relevância na Pesquisa
65.94%
O objetivo principal deste trabalho é propor algoritmos para identificação de series temporais e de sistemas lineares multivariáveis estocásticos variantes no tempo no espaço de estado. Para isto primeiramente investigamos fundamentos teóricos, apresentando alguns conceitos básicos de series temporais, sistemas, elementos de identificação, modelos no espaço de estado e identificação variante no tempo. Dois algoritmos são propostos, analisados e implementados, o que chamamos MOESP-AOKIVAR baseado no MOESP (Multivariable Output-Error State space) e o que chamamos AOKI-VAR baseado no algoritmo proposto por Masanao Aoki. Os algoritmos são avaliados sobre "benchmarks". Finalmente exemplos são apresentados bem como discussões sobre validação, previsão e modelagem de séries temporais e a modelagem de sistemas multivariáveis estocásticos variantes no tempo, esperando contribuir no estudo deste tipo de sinais e sistemas.; The main objective of this work is to propose algorithms for identifying non-stationary time series and multivariable time-varying linear stochastic systems in the state space. In order first to do this we investigate theoretical foundations, presenting some basic concepts of time series, systems, identification elements...

A comparison between single site modeling and multiple site modeling approaches using Kalman filtering

Monteiro, Magda; Costa, Marco
Fonte: AIP Publishing Publicador: AIP Publishing
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
55.89%
This work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single- site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set.

Discrimination of water quality monitoring sites in River Vouga using a mixed-effect state space model

Costa, Marco; Monteiro, Magda
Fonte: Springer Berlin Heidelberg Publicador: Springer Berlin Heidelberg
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
75.73%
The surface water quality monitoring is an important concern of public organizations due to its relevance to the public health. Statistical methods are taken as consistent and essential tools in the monitoring procedures in order to prevent and identify environmental problems. This work presents the study case of the hydrological basin of the river Vouga, in Portugal. The main goal is discriminate the water monitoring sites using the monthly dissolved oxygen concentration dataset between January 2002 and May 2013. This is achieved through the extraction of trend and seasonal components in a linear mixed-effect state space model. The parameters estimation is performed with both maximum likelihood method and distribution-free estimators in a two-step procedure. The application of the Kalman smoother algorithm allows to obtain predictions of the structural components as trend and seasonality. The water monitoring sites are discriminated through the structural components by a hierarchical agglomerative clustering procedure. This procedure identified different homogenous groups relatively to the trend and seasonality components and some characteristics of the hydrological basin are presented in order to support the results.

Dynamic linear modeling of homogenized monthly temperature in Lisbon

Costa, Marco; Monteiro, Magda
Fonte: Nova Science Publishers Publicador: Nova Science Publishers
Tipo: Parte de Livro
ENG
Relevância na Pesquisa
55.79%
This chapter focuses on the statistical modeling of the homogenized monthly ave- rage temperature data of Lisbon from 1856 to 2008. An exploratory analysis was performed using linear regression models which indicates the need of considering the temporal dependency and some flexibility in the trend modeling. In order to incorpo- rate the properties of the data it was adopted a dynamic linear models with a fixed effect component. The model was fitted by a two-step procedure which combines the least squares method and the maximum likelihood estimation in the state space framework. The results indicated an average increase of the homogenized monthly temperature in Lisbon in about 0.427oC per century, between 1856 to 2008. Additionally, smoother predictions of the stochastic slopes indicated that the rise of temperature moderately changes according to the month, higher linear increases occurred in the winter months and lower increases occurred in the summer months.

State-space decoding of primary afferent neuron firing rates

Wagenaar, JB; Ventura, V; Weber, DJ
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
55.76%
Kinematic state feedback is important for neuroprostheses to generate stable and adaptive movements of an extremity. State information, represented in the firing rates of populations of primary afferent neurons, can be recorded at the level of the dorsal root ganglia (DRG). Previous work in cats showed the feasibility of using DRG recordings to predict the kinematic state of the hind limb using reverse regression. Although accurate decoding results were attained, reverse regression does not make efficient use of the information embedded in the firing rates of the neural population. In this paper, we present decoding results based on state-space modeling, and show that it is a more principled and more efficient method for decoding the firing rates in an ensemble of primary afferent neurons. In particular, we show that we can extract confounded information from neurons that respond to multiple kinematic parameters, and that including velocity components in the firing rate models significantly increases the accuracy of the decoded trajectory. We show that, on average, state-space decoding is twice as efficient as reverse regression for decoding joint and endpoint kinematics.

State-space modeling and optimal control of ship motions in a seastate

Ulusoy, Talha
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Tese de Doutorado Formato: 101 leaves
ENG
Relevância na Pesquisa
55.82%
In this thesis, a new state-space model and motion control algorithm are developed from first principles for the improvement of the seakeeping performance of high-speed vessels equipped with lifting appendages that are actively controlled in regular and random waves. A ship at sea can experience all the translational and rotational modes of motion that are undesirable, yet unavoidable. These motions have been of great concern to the navies and other organizations engaged in shipping for decades and need to be dealt with through the use of a control system. In this work, a new general purpose state-space control-oriented time domain model for the ship motions is introduced. A discrete auto-regressive state-space model is developed using the state-of-the-art linear seakeeping simulation method SWAN. Novel features of this state-space model are its ability to capture all free-surface memory effects present in the seakeeping problem, its coupling with the theoretical framework of Linear Quadratic (LQ) controllers and its efficient implementation.; (cont.) The development from first principles of a reliable ship motion control simulation method based on SWAN and its coupling with LQ controllers used to actively regulate the angle of attack of lifting appendages...

2.141 Modeling and Simulation of Dynamic Systems, Fall 2002; Modeling and Simulation of Dynamic Systems

Hogan, Neville John
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
EN-US
Relevância na Pesquisa
55.84%
Mathematical modeling of complex engineering systems at a level of detail compatible with the design and implementation of modern control systems. Wave-like and diffusive energy transmission systems. Multiport energy storing fields and dissipative fields; consequences of symmetry and asymmetry. Nonlinear mechanics and canonical transformation theory. Examples will include mechanisms, electromechanical transducers, electronic systems, fluid systems, thermal systems, compressible flow processes, chemical processes. Description from course home page: This course deals with modeling multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics covered include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms, nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission.

Dynamic Modeling and Analysis of Single-Stage Boost Inverters under Normal and Abnormal Conditions

Kashefi Kaviani, Ali
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Relevância na Pesquisa
55.81%
Inverters play key roles in connecting sustainable energy (SE) sources to the local loads and the ac grid. Although there has been a rapid expansion in the use of renewable sources in recent years, fundamental research, on the design of inverters that are specialized for use in these systems, is still needed. Recent advances in power electronics have led to proposing new topologies and switching patterns for single-stage power conversion, which are appropriate for SE sources and energy storage devices. The current source inverter (CSI) topology, along with a newly proposed switching pattern, is capable of converting the low dc voltage to the line ac in only one stage. Simple implementation and high reliability, together with the potential advantages of higher efficiency and lower cost, turns the so-called, single-stage boost inverter (SSBI), into a viable competitor to the existing SE-based power conversion technologies. The dynamic model is one of the most essential requirements for performance analysis and control design of any engineering system. Thus, in order to have satisfactory operation, it is necessary to derive a dynamic model for the SSBI system. However, because of the switching behavior and nonlinear elements involved...

State-space modeling, system identification and control of a 4th order rotational mechanical system

Anderson, Jeremiah P.
Fonte: Monterey, California: Naval Postgraduate School Publicador: Monterey, California: Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xxii, 93 p. : ill. ;
Relevância na Pesquisa
55.79%
Approved for public release, distribution unlimited; In this thesis, a 4th order rotational mechanical plant provided by Educational Control Products is modeled from first principles and represented in state-space form. Identification of the state-space parameters was accomplished using the parameter estimation function in Matlab's System Identification Toolbox utilizing experimental input/output data. The identified model was then constructed in Simulink and the accuracy of the identified model parameters was studied. The open loop stability of the plant, as well as its controllability and observability were analyzed to determine the applicability of a pole placement control strategy. Based on the results of this analysis, a full state variable feedback controller was investigated to place the system's poles such that a rotational disk would perfectly track a step angle input with less than five percent overshoot and have less than a one second settling time, with no steady-state error. A refinement of this controller, to include an observer to estimate the system states, was also investigated. Finally, the results of this work are summarized and presented as a series of laboratories applicable to a course in state-space design.; US Navy (USN) author.

Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.

Djegnéné, Gbowan Barnabé
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
FR
Relevância na Pesquisa
65.8%
The first two articles build procedures to simulate vector of univariate states and estimate parameters in nonlinear and non Gaussian state space models. We propose state space speci fications that offer more flexibility in modeling dynamic relationship with latent variables. Our procedures are extension of the HESSIAN method of McCausland[2012]. Thus, they use approximation of the posterior density of the vector of states that allow to : simulate directly from the state vector posterior distribution, to simulate the states vector in one bloc and jointly with the vector of parameters, and to not allow data augmentation. These properties allow to build posterior simulators with very high relative numerical efficiency. Generic, they open a new path in nonlinear and non Gaussian state space analysis with limited contribution of the modeler. The third article is an essay in commodity market analysis. Private firms coexist with farmers' cooperatives in commodity markets in subsaharan african countries. The private firms have the biggest market share while some theoretical models predict they disappearance once confronted to farmers cooperatives. Elsewhere, some empirical studies and observations link cooperative incidence in a region with interpersonal trust...

STATE-SPACE SOLUTIONS TO THE DYNAMIC MAGNETOENCEPHALOGRAPHY INVERSE PROBLEM USING HIGH PERFORMANCE COMPUTING

Long, Christopher J.; Purdon, Patrick L.; Temereanca, Simona; Desai, Neil U.; Hämäläinen, Matti S.; Brown, Emery N.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/06/2011 EN
Relevância na Pesquisa
55.79%
Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an L2 regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intra-cortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process. We show that the Kalman filter (KF) and the Kalman smoother [also known as the fixed-interval smoother (FIS)] may be used to solve the ensuing high-dimensional state-estimation problem. Using a well-known relationship between Bayesian estimation and Kalman filtering...

Linear State Space Modeling of Gamma-Ray Burst Lightcurves

Band, David; Koenig, Michael; Chernenko, Anton
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 26/11/1997
Relevância na Pesquisa
65.79%
Linear State Space Modeling determines the hidden autoregressive (AR) process in a noisy time series; for an AR process the time series' current value is the sum of current stochastic ``noise'' and a linear combination of previous values. We present preliminary results from modeling a sample of 4 channel BATSE LAD lightcurves. We determine the order of the AR process necessary to model the bursts. The comparison of decay constants for different energy bands shows that structure decays more rapidly at high energy. The resulting models can be interpreted physically; for example, they may reveal the response of the burst emission region to the injection of energy.; Comment: 5 pages, 2 figures, AIPPROC LaTeX, to appear in "Gamma-Ray Bursts, 4th Huntsville Symposium," eds. C. Meegan, R. Preece and T. Koshut

State-space modeling of dynamic genetic networks

Lotsi, Anani; Wit, Ernst
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
55.73%
The genomic reality is a highly complex and dynamic system. The recent development of high-throughput technologies has enabled researchers to measure the abundance of many genes (in the order of thousands) simultaneously. The challenge is to unravel from such measurements, gene/protein or gene/gene or protein/ protein interactions and key biological features of cellular systems. Our goal is to devise a method for inferring transcriptional or gene regulatory networks from high-throughput data sources such as gene expression microarrays with potentially hidden states, such as unmeasured transcription factors (TFs), which satisfies certain Markov properties. We propose a dynamic state space representation. Our method is based on an EM algorithm with an incorporated Kalman smoothing algorithm in the E-step, a bootstrap for confidence intervals to infer the networks and the AIC for model selection. The state space model is an approach with proven effectiveness to reverse engineer transcriptional networks. The proposed method is applied to time course microarray data obtained from well established T-cell. When we applied the method to the T-cell data, we obtained 4, as the optimum number of hidden states. Our results support interesting biological properties in the family of Jun genes. The following genes were mostly seen as regulatory genes. These genes includes FYB...

Bayesian Nonparametric Dynamic State Space Modeling with Circular Latent States

Mazumder, Satyaki; Bhattacharya, Sourabh
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
65.89%
State space models are well-known for their versatility in modeling dynamic systems that arise in various scientific disciplines. Although parametric state space models are well studied, nonparametric approaches are much less explored in comparison. In this article we propose a novel Bayesian nonparametric approach to state space modeling assuming that both the observational and evolutionary functions are unknown and are varying with time; crucially, we assume that the unknown evolutionary equation describes dynamic evolution of some latent circular random variable. Based on appropriate kernel convolution of the standard Wiener process we model the time-varying observational and evolutionary functions as suitable Gaussian processes that take both linear and circular variables as arguments. Additionally, for the time-varying evolutionary function, we wrap the Gaussian process thus constructed around the unit circle to form an appropriate circular Gaussian process. We show that our process thus created satisfies desirable properties. For the purpose of inference we develop an MCMC based methodology combining Gibbs sampling and Metropolis-Hastings algorithms. Applications to a simulated data set, a real wind speed data set and a real ozone data set demonstrated quite encouraging performances of our model and methodologies.; Comment: A revised version...

Bayesian Analysis and Computational Methods for Dynamic Modeling

Niemi, Jarad
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação Formato: 2993798 bytes; application/pdf
Publicado em //2009 EN_US
Relevância na Pesquisa
56.01%

Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient methods for Bayesian inference for simultaneous estimation of latent states and unknown fixed parameters.

Chapter 1 introduces state space models and methods of inference in these models. Chapter 2 describes a novel method for jointly sampling the entire latent state vector in a nonlinear Gaussian state space model using a computationally efficient adaptive mixture modeling procedure. This method is embedded in an overall Markov chain Monte Carlo algorithm for estimating fixed parameters as well as states. In Chapter 3 the method of the previous chapter is implemented in a few illustrative

nonlinear models and compared to standard existing methods. This chapter also looks at the effect of the number of mixture components as well as length of the time series on the efficiency of the method. I then turn to an biological application in Chapter 4. I discuss modeling choices as well as derivation of the state space model to be used in this application. Parameter and state estimation are analyzed in these models for both simulated and real data. Chapter 5 extends the methodology introduced in Chapter 2 from nonlinear Gaussian models to general state space models. The method is then applied to a financial

stochastic volatility model on US $ - British £ exchange rates. Bayesian inference in the previous chapter is accomplished through Markov chain Monte Carlo which is suitable for batch analyses...