Results 1  10
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12
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
Abstract

Cited by 1051 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses RaoBlackwellisation in order to take advantage of the analytic structure present in some important classes of statespace models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
The Unscented Particle Filter
, 2000
"... In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available info ..."
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Cited by 211 (8 self)
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In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.
Particle Filters for State Space Models With the Presence of Static Parameters
, 2002
"... In this paper particle filters for dynamic state space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be ..."
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Cited by 63 (0 self)
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In this paper particle filters for dynamic state space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, realtime applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some lowdimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state space models. Marginalizing the static parameters avoids the problem of impoverishment which typically occur when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results.
Sequential Monte Carlo Methods to Train Neural Network Models
, 2000
"... We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/ sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequentia ..."
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Cited by 45 (7 self)
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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/ sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving online, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the onestepahead probability density functions of the options prices.
Approximation and Limit Results for Nonlinear Filters over an Infinite Time Interval: Part II, Random Sampling Algorithms
"... The paper is concerned with approximations to nonlinear filtering problems that are of interest over a very long time interval. Since the optimal filter can rarely be constructed, one needs to compute with numerically feasible approximations. The signal model can be a jumpdiffusion, reflected or no ..."
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Cited by 22 (9 self)
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The paper is concerned with approximations to nonlinear filtering problems that are of interest over a very long time interval. Since the optimal filter can rarely be constructed, one needs to compute with numerically feasible approximations. The signal model can be a jumpdiffusion, reflected or not. The observations can be taken either in discrete or continuous time. The cost of interest is the pathwise error per unit time over a long time interval. In a previous paper of the authors [2], it was shown, under quite reasonable conditions on the approximating filter and on the signal and noise processes that (as time, bandwidth, process and filter approximation, etc.) go to their limit in any way at all, the limit of the pathwise average costs per unit time is just what one would get if the approximating processes were replaced by their ideal values and the optimal filter were used. When suitable approximating filters cannot be readily constructed due to excessive computational requirem...
NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
, 2002
"... We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regressio ..."
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Cited by 21 (0 self)
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We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regression approaches, noisy measurements can produce inaccurate parameter estimates. Another problem is that for chaotic systems the cost functions that have to be minimized to estimate states and parameters are so complex that common optimization routines may fail. We show that the inclusion of information about the timecontinuous nature of the underlying trajectories can improve parameter estimation considerably. Two approaches, which take into account both the errorsinvariables problem and the problem of complex cost functions, are described in detail: shooting approaches and recursive estimation techniques. Both are demonstrated on numerical examples.
Sequential Monte Carlo Methods For Optimisation Of Neural Network Models
, 1998
"... We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of both computational time and accuracy, the hybrid SIR is a clear improvement over conventional seque ..."
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Cited by 13 (0 self)
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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of both computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimisation strategy, which allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving online, nonlinear and nonGaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the onestepahead probability density functions of the options prices.
Monte Carlo algorithms and asymptotic problems in nonlinear filtering
 To Appear in Stochastics in Finite/Infinite Dimensions (Volume in honor of Gopinath Kallianpur
, 1999
"... This paper is an extension of [4], which dealt with a wide variety of approximations to optimal nonlinear filters over long time intervals, where ..."
Abstract

Cited by 9 (3 self)
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This paper is an extension of [4], which dealt with a wide variety of approximations to optimal nonlinear filters over long time intervals, where
Particle Filtering Methods for Acoustic Source . . .
, 2004
"... The task of acoustic source tracking plays an important role in many practical speech acquisition systems. This research presents an extensive study of sequential Monte Carlo methods applied to the source localisation problem, based on the signals received at an array of microphones. A general frame ..."
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Cited by 8 (0 self)
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The task of acoustic source tracking plays an important role in many practical speech acquisition systems. This research presents an extensive study of sequential Monte Carlo methods applied to the source localisation problem, based on the signals received at an array of microphones. A general framework for acoustic source localisation using particle filtering is proposed, and four di#erent algorithms that fit within this framework are subsequently developed. To assess the performance of these new methods, statistical simulations are carried out using both synthetic and reallife samples of audio data. The simulation results demonstrate the superiority of an approach based on sequential estimation. The resulting particle filters are shown to drastically outperform traditional acoustic source localisation methods. Further
Intelligent Acquisition and Learning of Fluorescence Microscope Data Models
"... Abstract—We propose a mathematical framework and algorithms both to build accurate models of fluorescence microscope time series, as well as to design intelligent acquisition systems based on these models. Model building allows the information contained in the 2D and 3D time series to be presented ..."
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Cited by 5 (2 self)
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Abstract—We propose a mathematical framework and algorithms both to build accurate models of fluorescence microscope time series, as well as to design intelligent acquisition systems based on these models. Model building allows the information contained in the 2D and 3D time series to be presented in a more useful and concise form than the raw image data. This is particularly relevant as the trend in biology tends more and more towards highthroughput applications, and the resulting increase in the amount of acquired image data makes visual inspection impractical. The intelligent acquisition system uses an active learning approach to choose the acquisition regions that let us build our model most efficiently, resulting in a shorter acquisition time, as well as a reduction of the amount of photobleaching and phototoxicity incurred during acquisition. We validate our methodology by modeling object motion within a cell. For intelligent acquisition, we propose a set of algorithms to evaluate the information contained in a given acquisition region, as well as the costs associated with acquiring this region in terms of the resulting photobleaching and phototoxicity and the amount of time taken for acquisition. We use these algorithms to determine an acquisition strategy: where and when to acquire, as well as when to stop acquiring. Results, both on synthetic as well as real data, demonstrate accurate model building and large efficiency gains during acquisition. Index Terms—Active learning, fluorescence microscopy, image modeling, intelligent acquisition, particle filter. I.