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Relevant bibliographies by topics / Sequential Monte Carlo Filter

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Academic literature on the topic 'Sequential Monte Carlo Filter'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Sequential Monte Carlo Filter"

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Hanif, Ayub, and Robert Elliott Smith. "State Space Modeling & Bayesian Inference with Computational Intelligence." New Mathematics and Natural Computation 11, no. 01 (March 2015): 71–101. http://dx.doi.org/10.1142/s1793005715500040.

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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of nonlinear non-Gaussian dynamical systems. It enables us to reason under uncertainty and addresses shortcomings underlying deterministic systems and control theories which do not provide sufficient means of performing analysis and design. In addition, parametric techniques such as the Kalman filter and its extensions, though they are computationally efficient, do not reliably compute states and cannot be used to learn stochastic problems. We review recursive Bayesian estimation using sequential Monte Carlo methods highlighting open problems. Primary of these is the weight degeneracy and sample impoverishment problem. We proceed to detail synergistic computational intelligence sequential Monte Carlo methods which address this. We find that imbuing sequential Monte Carlos with computational intelligence has many advantages when applied to many application and problem domains.

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Du, Yun Ming, Bing Bing Yan, and Yong Cheng Jiang. "Face Tracking Algorithm Based on Sequential Monte Carlo Filter." Advanced Materials Research 430-432 (January 2012): 1777–81. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1777.

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Incorporating color distribution and spatial layout, this paper proposes a sequential Monte Carlo filter posterior tracking algorithm using color and spatial information in HSV color space. The target model is defined by the spatial color information of the tracking face region. By computing the characteristic distance between sample and target, different weights associated with every sample and the posterior of state vector can be computed. The samples distribution trends to the state distribution, whose validity is guaranteed by the strong law of large numbers. The tracking results using weighted samples are given in simulation. Experimental results show the probabilistic approach is simple and computationally efficient. In addition, this algorithm based on the sequential Monte Carlo filter could predict the location of face and track its trajectory satisfactorily in various complex conditions.

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Kitagawa, Genshiro. "Computational aspects of sequential Monte Carlo filter and smoother." Annals of the Institute of Statistical Mathematics 66, no. 3 (March 4, 2014): 443–71. http://dx.doi.org/10.1007/s10463-014-0446-0.

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Cong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong, and Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6872–77. http://dx.doi.org/10.1166/jctn.2016.5640.

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As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

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Freitas, J. F. G. de, M. Niranjan, A. H. Gee, and A. Doucet. "Sequential Monte Carlo Methods to Train Neural Network Models." Neural Computation 12, no. 4 (April 1, 2000): 955–93. http://dx.doi.org/10.1162/089976600300015664.

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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 on-line, 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 one-step-ahead probability density functions of the options prices.

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Hong Yoon, Ju, Du Yong Kim, and Kuk-Jin Yoon. "Efficient importance sampling function design for sequential Monte Carlo PHD filter." Signal Processing 92, no. 9 (September 2012): 2315–21. http://dx.doi.org/10.1016/j.sigpro.2012.01.010.

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Pulido, Manuel, and Peter Jan van Leeuwen. "Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter." Journal of Computational Physics 396 (November 2019): 400–415. http://dx.doi.org/10.1016/j.jcp.2019.06.060.

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Beskos, Alexandros, Dan Crisan, Ajay Jasra, Kengo Kamatani, and Yan Zhou. "A stable particle filter for a class of high-dimensional state-space models." Advances in Applied Probability 49, no. 1 (March 2017): 24–48. http://dx.doi.org/10.1017/apr.2016.77.

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Abstract We consider the numerical approximation of the filtering problem in high dimensions, that is, when the hidden state lies in ℝd with large d. For low-dimensional problems, one of the most popular numerical procedures for consistent inference is the class of approximations termed particle filters or sequential Monte Carlo methods. However, in high dimensions, standard particle filters (e.g. the bootstrap particle filter) can have a cost that is exponential in d for the algorithm to be stable in an appropriate sense. We develop a new particle filter, called the space‒time particle filter, for a specific family of state-space models in discrete time. This new class of particle filters provides consistent Monte Carlo estimates for any fixed d, as do standard particle filters. Moreover, when there is a spatial mixing element in the dimension of the state vector, the space‒time particle filter will scale much better with d than the standard filter for a class of filtering problems. We illustrate this analytically for a model of a simple independent and identically distributed structure and a model of an L-Markovian structure (L≥ 1, L independent of d) in the d-dimensional space direction, when we show that the algorithm exhibits certain stability properties as d increases at a cost 𝒪(nNd2), where n is the time parameter and N is the number of Monte Carlo samples, which are fixed and independent of d. Our theoretical results are also supported by numerical simulations on practical models of complex structures. The results suggest that it is indeed possible to tackle some high-dimensional filtering problems using the space‒time particle filter that standard particle filters cannot handle.

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Ahmed, Imtiaz. "Dolphin Whistle Track Estimation Using Sequential Monte Carlo Probability Hypothesis Density Filter." Dhaka University Journal of Science 62, no. 1 (February 7, 2015): 17–20. http://dx.doi.org/10.3329/dujs.v62i1.21954.

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This article focuses on possible automation of dolphin whistle track estimation process within the context of Multiple Target Tracking (MTT). It provides automatic whistle track estimation from raw hydrophone measurements using the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. Hydrophone measurements for three different types of species namely bottlenose dolphin (Tursiops truncates), common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba) have been used to benchmark the tracking performance of the SMC-PHD filter against three major challenges- the presence of multiple whistles, spontaneous death/birth of whistles and multiple whistles crossing each other. Quantitative analysis of the whistle track estimation accuracy is not possible since there is no ground truth type track for the dolphin whistles. Hence visual inspection of estimated tracks has been used corroborate the satisfactory tracking performance in the presence of all three challenges. DOI: http://dx.doi.org/10.3329/dujs.v62i1.21954 Dhaka Univ. J. Sci. 62(1): 17-20, 2014 (January)

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Thulin, Kristian, Geir Nævdal, Hans Julius Skaug, and Sigurd Ivar Aanonsen. "Quantifying Monte Carlo Uncertainty in the Ensemble Kalman Filter." SPE Journal 16, no. 01 (October 27, 2010): 172–82. http://dx.doi.org/10.2118/123611-pa.

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Summary The ensemble Kalman filter (EnKF) is currently considered one of the most promising methods for conditioning reservoir-simulation models to production data. The EnKF is a sequential Monte Carlo method based on a low-rank approximation of the system covariance matrix. The posterior probability distribution of model variables may be estimated from the updated ensemble, but, because of the low-rank covariance approximation, the updated ensemble members become correlated samples from the posterior distribution. We suggest using multiple EnKF runs, each with a smaller ensemble size, to obtain truly independent samples from the posterior distribution. This allows a pointwise confidence interval to be constructed for the posterior cumulative distribution function (CDF). We investigate the methodology for finding an optimal combination of ensemble batch size n and number of EnKF runs m while keeping the total number of ensemble members n×m constant. The optimal combination of n and m is found through minimizing the integrated mean-square error (MSE) for the CDFs. We illustrate the approach on two models, first a small linear model and then a synthetic 2D model inspired by petroleum applications. In the latter case, we choose to define an EnKF run with 10,000 ensemble members as having zero Monte Carlo error. The proposed methodology should be applicable also to larger, more-realistic models.

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Dissertations / Theses on the topic "Sequential Monte Carlo Filter"

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Fearnhead, Paul. "Sequential Monte Carlo methods in filter theory." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299043.

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Arnold, Andrea. "Sequential Monte Carlo Parameter Estimation for Differential Equations." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396617699.

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Gebart, Joakim. "GPU Implementation of the Particle Filter." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94190.

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This thesis work analyses the obstacles faced when adapting the particle filtering algorithm to run on massively parallel compute architectures. Graphics processing units are one example of massively parallel compute architectures which allow for the developer to distribute computational load over hundreds or thousands of processor cores. This thesis studies an implementation written for NVIDIA GeForce GPUs, yielding varying speed ups, up to 3000% in some cases, when compared to the equivalent algorithm performed on CPU. The particle filter, also known in the literature as sequential Monte-Carlo methods, is an algorithm used for signal processing when the system generating the signals has a highly nonlinear behaviour or non-Gaussian noise distributions where a Kalman filter and its extended variants are not effective. The particle filter was chosen as a good candidate for parallelisation because of its inherently parallel nature. There are, however, several steps of the classic formulation where computations are dependent on other computations in the same step which requires them to be run in sequence instead of in parallel. To avoid these difficulties alternative ways of computing the results must be used, such as parallel scan operations and scatter/gather methods. Another area where parallel programming still is not widespread is the area of pseudo-random number generation. Pseudo-random numbers are required by the algorithm to simulate the process noise as well as for avoiding the particle depletion problem using a resampling step. In this thesis a recently published counter-based pseudo-random number generator is used.

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Tumuluri, Uma. "Nonlinear State Estimation in Polymer Electrolyte Membrane Fuel Cells." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1231961499.

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Noh, Seong Jin. "Sequential Monte Carlo methods for probabilistic forecasts and uncertainty assessment in hydrologic modeling." 京都大学 (Kyoto University), 2013. http://hdl.handle.net/2433/170084.

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Woodard, Aaron Jacob, and Aaron Jacob Woodard. "Bayesian Estimation of a Single Mass Concentration Within an Asteroid." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625702.

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Orbit determination has long relied on the use of the Kalman filter, or specifically the extended Kalman filter, as a means of accurately navigating spacecraft. With the advent of cheaper, more powerful computers more accurate techniques such as the particle filter have been utilized. These Bayesian types of filters have in more recent years found their way to other applications. Dr. Furfaro and B. Gaudet have demonstrated the ability of the particle filter to accurately estimate the angular velocity, homogenous density, and rotation angle of a non-uniformly rotating ellipsoid shaped asteroid. This paper extends that work by utilizing a particle filter to accurately estimate the angular velocity and homogenous density of an ellipsoidal asteroid while simultaneously determining the location and mass of a mass concentration modeled as a point mass embedded within the asteroid. This work shows that by taking measurements in several locations around the asteroid, the asteroid's rotation state and mass distribution can be discerned.

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Yildirim, Berkin. "A Comparative Evaluation Of Conventional And Particle Filter Based Radar Target Tracking." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609043/index.pdf.

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In this thesis the radar target tracking problem in Bayesian estimation framework is studied. Traditionally, linear or linearized models, where the uncertainty in the system and measurement models is typically represented by Gaussian densities, are used in this area. Therefore, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo methods, i.e. particle filters, make it possible to utilize the inherent non-linear state relations and non-Gaussian noise models. Given the sufficient computational power, the particle filter can provide better results than Kalman filter based methods in many cases. A survey over relevant radar tracking literature is presented including aspects as estimation and target modeling. In various target tracking related estimation applications, particle filtering algorithms are presented.

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Al-Saadony, Muhannad. "Bayesian stochastic differential equation modelling with application to finance." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1530.

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In this thesis, we consider some popular stochastic differential equation models used in finance, such as the Vasicek Interest Rate model, the Heston model and a new fractional Heston model. We discuss how to perform inference about unknown quantities associated with these models in the Bayesian framework. We describe sequential importance sampling, the particle filter and the auxiliary particle filter. We apply these inference methods to the Vasicek Interest Rate model and the standard stochastic volatility model, both to sample from the posterior distribution of the underlying processes and to update the posterior distribution of the parameters sequentially, as data arrive over time. We discuss the sensitivity of our results to prior assumptions. We then consider the use of Markov chain Monte Carlo (MCMC) methodology to sample from the posterior distribution of the underlying volatility process and of the unknown model parameters in the Heston model. The particle filter and the auxiliary particle filter are also employed to perform sequential inference. Next we extend the Heston model to the fractional Heston model, by replacing the Brownian motions that drive the underlying stochastic differential equations by fractional Brownian motions, so allowing a richer dependence structure across time. Again, we use a variety of methods to perform inference. We apply our methodology to simulated and real financial data with success. We then discuss how to make forecasts using both the Heston and the fractional Heston model. We make comparisons between the models and show that using our new fractional Heston model can lead to improve forecasts for real financial data.

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Johansson, Anders. "Acoustic Sound Source Localisation and Tracking : in Indoor Environments." Doctoral thesis, Blekinge Tekniska Högskola [bth.se], School of Engineering - Dept. of Signal Processing, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00401.

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With advances in micro-electronic complexity and fabrication, sophisticated algorithms for source localisation and tracking can now be deployed in cost sensitive appliances for both consumer and commercial markets. As a result, such algorithms are becoming ubiquitous elements of contemporary communication, robotics and surveillance systems. Two of the main requirements of acoustic localisation and tracking algorithms are robustness to acoustic disturbances (to maximise localisation accuracy), and low computational complexity (to minimise power-dissipation and cost of hardware components). The research presented in this thesis covers both advances in robustness and in computational complexity for acoustic source localisation and tracking algorithms. This thesis also presents advances in modelling of sound propagation in indoor environments; a key to the development and evaluation of acoustic localisation and tracking algorithms. As an advance in the field of tracking, this thesis also presents a new method for tracking human speakers in which the problem of the discontinuous nature of human speech is addressed using a new state-space filter based algorithm which incorporates a voice activity detector. The algorithm is shown to achieve superior tracking performance compared to traditional approaches. Furthermore, the algorithm is implemented in a real-time system using a method which yields a low computational complexity. Additionally, a new method is presented for optimising the parameters for the dynamics model used in a state-space filter. The method features an evolution strategy optimisation algorithm to identify the optimum dynamics’ model parameters. Results show that the algorithm is capable of real-time online identification of optimum parameters for different types of dynamics models without access to ground-truth data. Finally, two new localisation algorithms are developed and compared to older well established methods. In this context an analytic analysis of noise and room reverberation is conducted, considering its influence on the performance of localisation algorithms. The algorithms are implemented in a real-time system and are evaluated with respect to robustness and computational complexity. Results show that the new algorithms outperform their older counterparts, both with regards to computational complexity, and robustness to reverberation and background noise. The field of acoustic modelling is advanced in a new method for predicting the energy decay in impulse responses simulated using the image source method. The new method is applied to the problem of designing synthetic rooms with a defined reverberation time, and is compared to several well established methods for reverberation time prediction. This comparison reveals that the new method is the most accurate.

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Lee, Anthony. "Towards smooth particle filters for likelihood estimation with multivariate latent variables." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1547.

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In parametrized continuous state-space models, one can obtain estimates of the likelihood of the data for fixed parameters via the Sequential Monte Carlo methodology. Unfortunately, even if the likelihood is continuous in the parameters, the estimates produced by practical particle filters are not, even when common random numbers are used for each filter. This is because the same resampling step which drastically reduces the variance of the estimates also introduces discontinuities in the particles that are selected across filters when the parameters change. When the state variables are univariate, a method exists that gives an estimator of the log-likelihood that is continuous in the parameters. We present a non-trivial generalization of this method using tree-based o(N²) (and as low as O(N log N)) resampling schemes that induce significant correlation amongst the selected particles across filters. In turn, this reduces the variance of the difference between the likelihood evaluated for different values of the parameters and the resulting estimator is considerably smoother than naively running the filters with common random numbers. Importantly, in practice our methods require only a change to the resample operation in the SMC framework without the addition of any extra parameters and can therefore be used for any application in which particle filters are already used. In addition, excepting the optional use of interpolation in the schemes, there are no regularity conditions for their use although certain conditions make them more advantageous. In this thesis, we first introduce the relevant aspects of the SMC methodology to the task of likelihood estimation in continuous state-space models and present an overview of work related to the task of smooth likelihood estimation. Following this, we introduce theoretically correct resampling schemes that cannot be implemented and the practical tree-based resampling schemes that were developed instead. After presenting the performance of our schemes in various applications, we show that two of the schemes are asymptotically consistent with the theoretically correct but unimplementable methods introduced earlier. Finally, we conclude the thesis with a discussion.

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Books on the topic "Sequential Monte Carlo Filter"

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Chopin, Nicolas, and Omiros Papaspiliopoulos. An Introduction to Sequential Monte Carlo. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47845-2.

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Doucet, Arnaud, Nando Freitas, and Neil Gordon, eds. Sequential Monte Carlo Methods in Practice. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9.

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Bruno, Marcelo G. S. Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering. Morgan & Claypool Publishers, 2013.

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Arnaud, Doucet, De Freitas Nando, and Gordon Neil 1967-, eds. Sequential Monte Carlo methods in practice. New York: Springer, 2001.

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Chopin, Nicolas, and Omiros Papaspiliopoulos. An Introduction to Sequential Monte Carlo. Springer, 2020.

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Rubinstein, Reuven Y., Ad Ridder, and Radislav Vaisman. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley & Sons, Incorporated, John, 2013.

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Rubinstein, Reuven Y., Ad Ridder, and Radislav Vaisman. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley & Sons, Incorporated, John, 2013.

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(Foreword), A. Smith, Arnaud Doucet (Editor), Nando de Freitas (Editor), and Neil Gordon (Editor), eds. Sequential Monte Carlo Methods in Practice (Statistics for Engineering and Information Science). Springer, 2001.

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Fast Sequential Monte Carlo Methods for Counting and Optimization Wiley Series in Probability and Statistics. John Wiley & Sons Inc, 2014.

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Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.

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Book chapters on the topic "Sequential Monte Carlo Filter"

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McGinnity, Shaun, and George W. Irwin. "Manoeuvring Target Tracking Using a Multiple-Model Bootstrap Filter." In Sequential Monte Carlo Methods in Practice, 479–97. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_23.

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Musso, Christian, Nadia Oudjane, and Francois Gland. "Improving Regularised Particle Filters." In Sequential Monte Carlo Methods in Practice, 247–71. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_12.

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Pitt, Michael K., and Neil Shephard. "Auxiliary Variable Based Particle Filters." In Sequential Monte Carlo Methods in Practice, 273–93. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_13.

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Stavropoulos, Photis, and D. M. Titterington. "Improved Particle Filters and Smoothing." In Sequential Monte Carlo Methods in Practice, 295–317. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_14.

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Crisan, Dan. "Particle Filters — A Theoretical Perspective." In Sequential Monte Carlo Methods in Practice, 17–41. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_2.

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Fox, Dieter, Sebastian Thrun, Wolfram Burgard, and Frank Dellaert. "Particle Filters for Mobile Robot Localization." In Sequential Monte Carlo Methods in Practice, 401–28. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_19.

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Godsill, Simon, and Tim Clapp. "Improvement Strategies for Monte Carlo Particle Filters." In Sequential Monte Carlo Methods in Practice, 139–58. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_7.

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Bølviken, Erik, and Geir Storvik. "Deterministic and Stochastic Particle Filters in State-Space Models." In Sequential Monte Carlo Methods in Practice, 97–116. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_5.

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Lundén, Daniel, Johannes Borgström, and David Broman. "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages." In Programming Languages and Systems, 404–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_15.

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AbstractProbabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing research on such algorithms mainly concerns their implementation and efficiency, rather than the correctness of the algorithms themselves when applied in the context of expressive PPLs. To remedy this, we give a correctness proof for SMC methods in the context of an expressive PPL calculus, representative of popular PPLs such as WebPPL, Anglican, and Birch. Previous work have studied correctness of MCMC using an operational semantics, and correctness of SMC and MCMC in a denotational setting without term recursion. However, for SMC inference—one of the most commonly used algorithms in PPLs as of today—no formal correctness proof exists in an operational setting. In particular, an open question is if the resample locations in a probabilistic program affects the correctness of SMC. We solve this fundamental problem, and make four novel contributions: (i) we extend an untyped PPL lambda calculus and operational semantics to include explicit resample terms, expressing synchronization points in SMC inference; (ii) we prove, for the first time, that subject to mild restrictions, any placement of the explicit resample terms is valid for a generic form of SMC inference; (iii) as a result of (ii), our calculus benefits from classic results from the SMC literature: a law of large numbers and an unbiased estimate of the model evidence; and (iv) we formalize the bootstrap particle filter for the calculus and discuss how our results can be further extended to other SMC algorithms.

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Schikora, Marek, Wolfgang Koch, Roy Streit, and Daniel Cremers. "A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter." In Advances in Intelligent Signal Processing and Data Mining, 55–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28696-4_3.

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Conference papers on the topic "Sequential Monte Carlo Filter"

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Houssineau, Jeremie, Daniel E. Clark, and Pierre Del Moral. "A sequential Monte Carlo approximation of the HISP filter." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362584.

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Dogatovic, Marko, and Milorad Stanojevic. "Multipath mitigation of GPS signal using sequential Monte-Carlo filter." In TELSIKS 2009 - 2009 9th International Conference on Telecommunications in Modern Satellite, Cable, and Broadcasting Services. IEEE, 2009. http://dx.doi.org/10.1109/telsks.2009.5339420.

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Du, Yunming, Lili Yu, Lina Gai, and Yongcheng Jiang. "Analysis of nonlinear processing ability of sequential Monte Carlo filter." In 2018 4th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2018. http://dx.doi.org/10.1109/iccar.2018.8384690.

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Jing Yang, V. Kadirkamanathan, and S. A. Billings. "In vivo Intracellular Metabolite Dynamics Estimation by Sequential Monte Carlo Filter." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221248.

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Gao, Hongzhi, and Richard Green. "A sequential Monte Carlo method for particle filters." In 2008 23rd International Conference Image and Vision Computing New Zealand (IVCNZ). IEEE, 2008. http://dx.doi.org/10.1109/ivcnz.2008.4762108.

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Ba-Ngu Vo, S. Singh, and A. Doucet. "Sequential monte carlo implementation of the phd filter for multi-target tracking." In Proceedings of the Sixth International Conference of Information Fusion. IEEE, 2003. http://dx.doi.org/10.1109/icif.2003.177320.

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Mihaylova, L., D. Bull, D. Angelova, and N. Canagarajah. "Mobility tracking in cellular networks with sequential Monte Carlo filters." In 2005 7th International Conference on Information Fusion. IEEE, 2005. http://dx.doi.org/10.1109/icif.2005.1591843.

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Panta, Kusha, and Ba-Ngu Vo. "Convolution Kernels based Sequential Monte Carlo Approximation of the Probability Hypothesis Density (PHD) Filter." In 2007 Information, Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/idc.2007.374573.

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Edla, S., N. Kovvali, and A. Papandreou-Suppappola. "Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346915.

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Jiang, Tongyang. "Multiple-model Bernoulli filters—part II: A sequential Monte Carlo implementation." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554102.

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Reports on the topic "Sequential Monte Carlo Filter"

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Herbst, Edward, and Frank Schorfheide. Sequential Monte Carlo Sampling for DSGE Models. Cambridge, MA: National Bureau of Economic Research, June 2013. http://dx.doi.org/10.3386/w19152.

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Acton, Scott T., and Bing Li. A Sequential Monte Carlo Method for Real-time Tracking of Multiple Targets. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada532576.

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Meadors, Grant. Forecasting the Solar Wind with Sequential Monte Carlo Assimilation of Satellite Data [Slides]. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1770082.

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Amela, R., R. Badia, S. Böhm, R. Tosi, C. Soriano, and R. Rossi. D4.2 Profiling report of the partner’s tools, complete with performance suggestions. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.023.

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Abstract:

This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions. While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.

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