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Gotowa bibliografia na temat „Sequential Monte Carlo (SMC) method”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "Sequential Monte Carlo (SMC) method"

Wang, Liangliang, Shijia Wang i Alexandre Bouchard-Côté. "An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics". Systematic Biology 69, nr 1 (6.06.2019): 155–83. http://dx.doi.org/10.1093/sysbio/syz028.

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Abstract We describe an “embarrassingly parallel” method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis.

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Finke, Axel, Arnaud Doucet i Adam M. Johansen. "Limit theorems for sequential MCMC methods". Advances in Applied Probability 52, nr 2 (czerwiec 2020): 377–403. http://dx.doi.org/10.1017/apr.2020.9.

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AbstractBoth sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of algorithms which can be used to approximate expectations with respect to (a sequence of) probability distributions and their normalising constants. While SMC methods sample particles conditionally independently at each time step, sequential MCMC methods sample particles according to a Markov chain Monte Carlo (MCMC) kernel. Introduced over twenty years ago in [6], sequential MCMC methods have attracted renewed interest recently as they empirically outperform SMC methods in some applications. We establish an $\mathbb{L}_r$ -inequality (which implies a strong law of large numbers) and a central limit theorem for sequential MCMC methods and provide conditions under which errors can be controlled uniformly in time. In the context of state-space models, we also provide conditions under which sequential MCMC methods can indeed outperform standard SMC methods in terms of asymptotic variance of the corresponding Monte Carlo estimators.

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Cong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong i Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem". Journal of Computational and Theoretical Nanoscience 13, nr 10 (1.10.2016): 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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Abu Znaid, Ammar M. A., Mohd Yamani Idna Idris, Ainuddin Wahid Abdul Wahab, Liana Khamis Qabajeh i Omar Adil Mahdi. "Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review". Journal of Sensors 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/1430145.

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The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.

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Deng, Yue, Yongzhen Pei, Changguo Li i Bin Zhu. "Model Selection and Parameter Estimation for an Improved Approximate Bayesian Computation Sequential Monte Carlo Algorithm". Discrete Dynamics in Nature and Society 2022 (30.06.2022): 1–14. http://dx.doi.org/10.1155/2022/8969903.

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Model selection and parameter estimation are very important in many fields. However, the existing methods have many problems, such as low efficiency in model selection and inaccuracy in parameter estimation. In this study, we proposed a new algorithm named improved approximate Bayesian computation sequential Monte Carlo algorithm (IABC-SMC) based on approximate Bayesian computation sequential Monte Carlo algorithm (ABC-SMC). Using the IABC-SMC algorithm, given data and the set of two models including logistic and Gompertz models of infectious diseases, we obtained the best fitting model and the values of unknown parameters of the corresponding model. The simulation results showed that the IABC-SMC algorithm can quickly and accurately select a model that best matches the corresponding epidemic data among multiple candidate models and estimate the values of unknown parameters of model very accurately. We further compared the effects of IABC-SMC algorithm with that of ABC-SMC algorithm. Simulations showed that the IABC-SMC algorithm can improve the accuracy of estimated parameter values and the speed of model selection and also avoid the shortage of ABC-SMC algorithm. This study suggests that the IABC-SMC algorithm can be seen as a promising method for model selection and parameter estimation.

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Hsu, Kuo-Lin. "Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach". Journal of Hydroinformatics 13, nr 1 (2.04.2010): 25–35. http://dx.doi.org/10.2166/hydro.2010.044.

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Sequential Monte Carlo (SMC) methods are known to be very effective for the state and parameter estimation of nonlinear and non-Gaussian systems. In this study, SMC is applied to the parameter estimation of an artificial neural network (ANN) model for streamflow prediction of a watershed. Through SMC simulation, the probability distribution of model parameters and streamflow estimation is calculated. The results also showed the SMC approach is capable of providing reliable streamflow prediction under limited available observations.

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Weng, Zhipeng, Jinghua Zhou i Zhengdong Zhan. "Reliability Evaluation of Standalone Microgrid Based on Sequential Monte Carlo Simulation Method". Energies 15, nr 18 (14.09.2022): 6706. http://dx.doi.org/10.3390/en15186706.

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In order to analyze the influence of uncertainty and an operation strategy on the reliability of a standalone microgrid, a reliability evaluation method based on a sequential Monte Carlo (SMC) simulation was developed. Here, the duty cycles of a microturbine (MT), the stochastic performance of photovoltaics (PV), and wind turbine generators (WTG) were considered. Moreover, the time-varying load with random fluctuation was modeled. In this method, the available capacity of an energy storage system (ESS) was also comprehensively considered by the SMC simulation. Then, the reliability evaluation framework was established from the perspectives of probability, frequency, and duration, and reliability evaluation algorithms under different operation strategies were formulated. Lastly, the influence of WTG and PV penetration and equipment capacity on the reliability was evaluated in the test system. The results showed that the complementary characteristics of wind and solar and the enhancement of the equipment capacity can both improve the reliability; but, with the increase in the penetration rate of WTG and PV, more ESS capacity is needed to cope with the randomness of WTG and PV. In addition, load shedding minimization strategies can minimize the probability and the number of reductions and achieve optimal reliability, which can provide a reference for the formulation of microgrid operation strategies.

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Röder, Lenard L., Patrick Dewald, Clara M. Nussbaumer, Jan Schuladen, John N. Crowley, Jos Lelieveld i Horst Fischer. "Data quality enhancement for field experiments in atmospheric chemistry via sequential Monte Carlo filters". Atmospheric Measurement Techniques 16, nr 5 (7.03.2023): 1167–78. http://dx.doi.org/10.5194/amt-16-1167-2023.

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Abstract. In this study, we explore the applications and limitations of sequential Monte Carlo (SMC) filters to field experiments in atmospheric chemistry. The proposed algorithm is simple, fast, versatile and returns a complete probability distribution. It combines information from measurements with known system dynamics to decrease the uncertainty of measured variables. The method shows high potential to increase data coverage, precision and even possibilities to infer unmeasured variables. We extend the original SMC algorithm with an activity variable that gates the proposed reactions. This extension makes the algorithm more robust when dynamical processes not considered in the calculation dominate and the information provided via measurements is limited. The activity variable also provides a quantitative measure of the dominant processes. Free parameters of the algorithm and their effect on the SMC result are analyzed. The algorithm reacts very sensitively to the estimated speed of stochastic variation. We provide a scheme to choose this value appropriately. In a simulation study, O3, NO, NO2 and jNO2 are tested for interpolation and de-noising using measurement data of a field campaign. Generally, the SMC method performs well under most conditions, with some dependence on the particular variable being analyzed.

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Nakano, S., K. Suzuki, K. Kawamura, F. Parrenin i T. Higuchi. "A sequential Bayesian approach for the estimation of the age–depth relationship of Dome Fuji ice core". Nonlinear Processes in Geophysics Discussions 2, nr 3 (26.06.2015): 939–68. http://dx.doi.org/10.5194/npgd-2-939-2015.

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Abstract. A technique for estimating the age–depth relationship in an ice core and evaluating its uncertainty is presented. The age–depth relationship is mainly determined by the accumulation of snow at the site of the ice core and the thinning process due to the horizontal stretching and vertical compression of ice layers. However, since neither the accumulation process nor the thinning process are fully understood, it is essential to incorporate observational information into a model that describes the accumulation and thinning processes. In the proposed technique, the age as a function of depth is estimated from age markers and δ18O data. The estimation is achieved using the particle Markov chain Monte Carlo (PMCMC) method, in which the sequential Monte Carlo (SMC) method is combined with the Markov chain Monte Carlo method. In this hybrid method, the posterior distributions for the parameters in the models for the accumulation and thinning processes are computed using the Metropolis method, in which the likelihood is obtained with the SMC method. Meanwhile, the posterior distribution for the age as a function of depth is obtained by collecting the samples generated by the SMC method with Metropolis iterations. The use of this PMCMC method enables us to estimate the age–depth relationship without assuming either linearity or Gaussianity. The performance of the proposed technique is demonstrated by applying it to ice core data from Dome Fuji in Antarctica.

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Rusyda Roslan, Nur Nabihah, NoorFatin Farhanie Mohd Fauzi i Mohd Ikhwan Muhammad Ridzuan. "Variance reduction technique in reliability evaluation for distribution system by using sequential Monte Carlo simulation". Bulletin of Electrical Engineering and Informatics 11, nr 6 (1.12.2022): 3061–68. http://dx.doi.org/10.11591/eei.v11i6.3950.

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This paper discusses the need for variance reduction in simulations in order to reduce the time required to compute a simulation. The large and complex network is commonly evaluated using a large-scale Monte Carlo simulation. Unfortunately, due to the different sizes of the network, it takes some time to complete a simulation. However, variance reduction techniques (VRT) can help to solve the issues. The effect of VRT changes the behaviors of a simulation, particularly the time required to run the simulation. To evaluate the reliability indices, two sequential Monte Carlo (SMC) methods are used. SMC with VRT and SMC without VRT are the two options. The presence of VRT in the simulation distinguishes the two simulations. Finally, reliability indices: system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and customer average interruption duration index (CAIDI) will be calculated at the end of the simulation to determine the efficiency for the SMC with and without VRT. Overall, the SMC with VRT is more efficient because it is more convenient and saves time than the SMC without VRT.

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Rozprawy doktorskie na temat "Sequential Monte Carlo (SMC) method"

GONZATO, LUCA. "Application of Sequential Monte Carlo Methods to Dynamic Asset Pricing Models". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/295144.

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In questa tesi si considera l’applicazione di metodi Monte Carlo sequenziali per modelli di asset pricing di tipo dinamico. Il primo capitolo della tesi presenta una panoramica generale sui metodi Monte Carlo sequenziali. Nello specifico, partendo da metodi Monte Carlo standard si giunge fino allo stato dell’arte per quanto riguarda i metodi Monte Carlo sequenziali. Il secondo capitolo costituisce una review della letteratura sui metodi di simulazione esatta per processi di Hawkes. Dall’analisi svolta si evince che lo schema proposto da Dassios e Zaho (2013) performa meglio degli altri algoritmi, incluso il più noto metodo “thinning” proposto da Ogata (1981). Questo capitolo serve inoltre come introduzione ai processi di salto di tipo auto eccitante, che saranno oggetto di studio del Capitolo 3. Nel terzo capitolo, quindi, viene proposto un nuovo modello diffusivo con salti auto eccitati per descrivere la dinamica del prezzo del petrolio. Il modello viene stimato implementando una recente metodologia di tipo Monte Carlo sequenziale utilizzando dati spot e futures. Dalla stima viene confermata la presenza di salti auto eccitati nel mercato del petrolio; questo conduce ad un migliore adattamento del modello ai dati e a migliori performance in termini di previsione dei futures rispetto ad un modello con intensità costante. Inoltre, vengono calcolate e discusse due strategie di copertura ottimali basate sul trading di contratti futures. La prima strategia è basata sulla minimizzazione della varianza, mentre la seconda tiene in considerazione anche la skewness. Viene infine proposto un confronto tra le due strategie in termini di efficacia della copertura Nel quarto capitolo si considera la stima di modelli a volatilità stocastica a tempo continuo basati su processi di Wishart, osservando portafogli di opzioni come in Orlowski (2019). In questo contesto la funzione di verosimiglianza non è nota esplicitamente, quindi verrà stimata ricorrendo a metodi Monte Carlo sequenziali. A questo proposito, i processi latenti vengono marginalizzati e la stima della verosimiglianza viene effettuata adattando metodi Monte Carlo sequenziali “controllati”, recentemente proposti da Heng et. Al. (2019). Dai risultati numerici si mostra come la metodologia proposta dia risultati decisamente migliori rispetto a metodi standard. Pertanto, l’elevata stabilità della metodologia proposta permetterà di costruire algoritmi per la stima congiunta di processi latenti e parametri utilizzando un approccio Bayesiano. Quest’ultimo step si traduce nel costruire un così detto SMC sampler, il quale è attualmente in fase di studio.
In this thesis we consider the application of Sequential Monte Carlo (SMC) methods to continuous-time asset pricing models. The first chapter of the thesis gives a self-contained overview on SMC methods. In particular, starting from basic Monte Carlo techniques we move to recent state of the art SMC algorithms. In the second chapter we review existing methods for the exact simulation of Hawkes processes. From our analysis we infer that the simulation scheme of Dassios and Zaho (2013) outperforms the other algorithms, including the most popular thinning method proposed by Ogata (1980). This chapter serves also as introduction to self-exciting jump processes, which are the subject of Chapter 3. Hence, in the third chapter we propose a new self-exciting jump diffusion model in order to describe oil price dynamics. We estimate the model by applying a state of the art SMC sampler on both spot and futures data. From the estimation results we find evidence of self-excitation in the oil market, which leads to an improved fit and a better out of sample futures forecasting performance with respect to jump-diffusion models with constant intensity. Furthermore, we compute and discuss two optimal hedging strategies based on futures trading. The optimality of the first hedging strategy proposed is based on the variance minimization, while the second strategy takes into account also the third-order moment contribution in considering the investors attitudes. A comparison between the two strategies in terms of hedging effectiveness is provided. Finally, in the fourth chapter we consider the estimation of continuous-time Wishart stochastic volatility models by observing portfolios of weighted options as in Orlowski (2019). In this framework we don't know the likelihood in closed-form; then we aim to estimate it using SMC techniques. To this end, we marginalize latent states and perform marginal likelihood estimation by adapting the recently proposed controlled SMC algorithm (Heng et. Al. 2019). From the numerical experiments we show that the proposed methodology gives much better results with respect to standard filtering techniques. Therefore, the great stability of our SMC method opens the door for effective joint estimation of latent states and unknown parameters in a Bayesian fashion. This last step amounts to design an SMC sampler based on a pseudo-marginal argument and is currently under preparation.

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Ozgur, Soner. "Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers". Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10518.

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Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical layer communications system models to estimate channel state information, to obtain soft information about transmitted symbols or multiple access interference, or to obtain estimates of all of these by joint estimation. Initially, we develop and analyze a multiple access technique utilizing mutually orthogonal complementary sets (MOCS) of sequences. These codes deliberately introduce inter-chip interference, which is naturally eliminated during processing at the receiver. However, channel impairments can destroy their orthogonality properties and additional processing becomes necessary. We utilize Monte Carlo algorithms to perform joint channel and symbol estimation for systems utilizing MOCS sequences as spreading codes. We apply Rao-Blackwellization to reduce the required number of particles. However, dense signaling constellations, multiuser environments, and the interchannel interference introduced by the spreading codes all increase the dimensionality of the symbol state space significantly. A full maximum likelihood solution is computationally expensive and generally not practical. However, obtaining the optimum solution is critical, and looking at only a part of the symbol space is generally not a good solution. We have sought algorithms that would guarantee that the correct transmitted symbol is considered, while only sampling a portion of the full symbol space. The performance of the proposed method is comparable to the Maximum Likelihood (ML) algorithm. While the computational complexity of ML increases exponentially with the dimensionality of the problem, the complexity of our approach increases only quadratically. Markovian structures such as the one imposed by MOCS spreading sequences can be seen in other physical layer structures as well. We have applied this partitioning approach with some modification to blind equalization of frequency selective fading channel and to multiple-input multiple output receivers that track channel changes. Additionally, we develop a method that obtains a metric for quantifying the convergence rate of Monte Carlo algorithms. Our approach yields an eigenvalue based method that is useful in identifying sources of slow convergence and estimation inaccuracy.

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Creal, Drew D. "Essays in sequential Monte Carlo methods for economics and finance /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7444.

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Lang, Lixin. "Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289.

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Kuhlenschmidt, Bernd. "On the stability of sequential Monte Carlo methods for parameter estimation". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709098.

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Skrivanek, Zachary. "Sequential Imputation and Linkage Analysis". The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039121487.

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Chen, Wen-shiang. "Bayesian estimation by sequential Monte Carlo sampling for nonlinear dynamic systems". Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1086146309.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xiv, 117 p. : ill. (some col.). Advisors: Bhavik R. Bakshi and Prem K. Goel, Department of Chemical Engineering. Includes bibliographical references (p. 114-117).

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Valdes, LeRoy I. "Analysis Of Sequential Barycenter Random Probability Measures via Discrete Constructions". Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3304/.

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Hill and Monticino (1998) introduced a constructive method for generating random probability measures with a prescribed mean or distribution on the mean. The method involves sequentially generating an array of barycenters that uniquely defines a probability measure. This work analyzes statistical properties of the measures generated by sequential barycenter array constructions. Specifically, this work addresses how changing the base measures of the construction affects the statististics of measures generated by the SBA construction. A relationship between statistics associated with a finite level version of the SBA construction and the full construction is developed. Monte Carlo statistical experiments are used to simulate the effect changing base measures has on the statistics associated with the finite level construction.

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Fuglesang, Rutger. "Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models". Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279847.

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This thesis investigates the possibility of using Sequential Monte Carlo methods (SMC) to create an online algorithm to infer properties from a dataset, such as unknown model parameters. Statistical inference from data streams tends to be difficult, and this is particularly the case for parametric models, which will be the focus of this paper. We develop a sequential Monte Carlo algorithm sampling sequentially from the model's posterior distributions. As a key ingredient of this approach, unknown static parameters are jittered towards the shrinking support of the posterior on the basis of an artificial Markovian dynamic allowing for correct pseudo-marginalisation of the target distributions. We then test the algorithm on a simple Gaussian model, a Gausian Mixture Model (GMM), as well as a variable dimension GMM. All tests and coding were done using Matlab. The outcome of the simulation is promising, but more extensive comparisons to other online algorithms for static parameter models are needed to really gauge the computational efficiency of the developed algorithm.
Detta examensarbete undersöker möjligheten att använda Sekventiella Monte Carlo metoder (SMC) för att utveckla en algoritm med syfte att utvinna parametrar i realtid givet en okänd modell. Då statistisk slutledning från dataströmmar medför svårigheter, särskilt i parameter-modeller, kommer arbetets fokus ligga i utvecklandet av en Monte Carlo algoritm vars uppgift är att sekvensiellt nyttja modellens posteriori fördelningar. Resultatet är att okända, statistiska parametrar kommer att förflyttas mot det krympande stödet av posterioren med hjälp utav en artificiell Markov dynamik, vilket tillåter en korrekt pseudo-marginalisering utav mål-distributionen. Algoritmen kommer sedan att testas på en enkel Gaussisk-modell, en Gaussisk mixturmodell (GMM) och till sist en GMM vars dimension är okänd. Kodningen i detta projekt har utförts i Matlab.

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Carr, Michael John. "Estimating parameters of a stochastic cell invasion model with Fluorescent cell cycle labelling using approximate Bayesian computation". Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226946/1/Michael_Carr_Thesis.pdf.

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Understanding the underlying mechanisms of melanoma cell behaviour is crucial to developing better drug treatment methods. In this thesis, we use advanced mathematical modelling and statistical inference techniques to obtain, for the first time, accurate estimates of the rates at which cells multiply and spread at multiple stages of the cell cycle. The mathematical model is fitted to data that uses fluorescent cell cycle labelling technology to visualise different phases of the cell cycle in real time. The accurately calibrated mathematical model enables a deeper understanding of cell behaviour and has potential for more precisely assessing treatment efficacy.

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Książki na temat "Sequential Monte Carlo (SMC) method"

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

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Doucet, Arnaud, Nando de Freitas, Neil Gordon i A. Smith. Sequential Monte Carlo Methods in Practice. Springer New York, 2010.

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

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

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

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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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Części książek na temat "Sequential Monte Carlo (SMC) method"

Lundén, Daniel, Johannes Borgström i David Broman. "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages". W 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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Lundén, Daniel, Joey Öhman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist i David Broman. "Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference". W Programming Languages and Systems, 29–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_2.

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AbstractProbabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling probabilistic checkpoints in PPLs through continuation-passing style transformations or non-preemptive multitasking—as is done in many popular PPLs—often disallows compilation to low-level languages required for high-performance platforms such as GPUs. To solve the checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs)—a simple and efficient approach to checkpoints in low-level languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs and illustrate its application when compiling Miking CorePPL—a high-level universal PPL—to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6$$\times $$ × speedups over state-of-the-art PPLs implementing SMC inference.

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Matsui, Atsushi, Simon Clippingdale i Takashi Matsumoto. "A Sequential Monte Carlo Method for Bayesian Face Recognition". W Lecture Notes in Computer Science, 578–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11815921_63.

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Lundén, Daniel, Gizem Çaylak, Fredrik Ronquist i David Broman. "Automatic Alignment in Higher-Order Probabilistic Programming Languages". W Programming Languages and Systems, 535–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_20.

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AbstractProbabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints—relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run—they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC.

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Panayirci, E., H. A. Çirpan, M. Moeneclaey i N. Noels. "Blind Phase Noise Estimation in OFDM Systems by Sequential Monte Carlo Method". W Multi-Carrier Spread-Spectrum, 483–90. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-4437-2_52.

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Schikora, Marek, Wolfgang Koch, Roy Streit i Daniel Cremers. "A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter". W 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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Verly, G. "Sequential Gaussian Simulation: A Monte Carlo Method for Generating Models of Porosity and Permeability". W Generation, Accumulation and Production of Europe’s Hydrocarbons III, 345–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77859-9_28.

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Jiang, Mingyan, i Dongfeng Yuan. "Blind Estimation of Fast Time-Varying Multi-antenna Channels Based on Sequential Monte Carlo Method". W Lecture Notes in Computer Science, 482–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538356_50.

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Reich, Sebastian. "A Guided Sequential Monte Carlo Method for the Assimilation of Data into Stochastic Dynamical Systems". W Recent Trends in Dynamical Systems, 205–20. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0451-6_10.

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Wu, Yaohao, Wenying Liu i Chen Liang. "A Reliability Evaluation Method of Generation and Transmission Systems Based on Sequential Monte-Carlo Simulation". W Lecture Notes in Electrical Engineering, 467–74. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4981-2_51.

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Streszczenia konferencji na temat "Sequential Monte Carlo (SMC) method"

Colac¸o, Marcelo J., Helcio R. B. Orlande, Wellington B. da Silva i George S. Dulikravich. "Application of a Bayesian Filter to Estimate Unknown Heat Fluxes in a Natural Convection Problem". W ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47652.

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Sequential Monte Carlo (SMC) or Particle Filter Methods, which have been originally introduced in the beginning of the 50’s, became very popular in the last few years in the statistical and engineering communities. Such methods have been widely used to deal with sequential Bayesian inference problems in fields like economics, signal processing, and robotics, among others. SMC Methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, named particles. These particles are propagated along time with a simple Sampling Importance distribution. Two advantages of this method are: they do not require the restrictive hypotheses of the Kalman filter, and can be applied to nonlinear models with non-Gaussian errors. This papers uses a SMC filter, namely the ASIR (Auxiliary Sampling Importance Resampling Filter) to estimate a heat flux in the wall of a square cavity undergoing a natural convection. Measurements, which contain errors, taken at the boundaries of the cavity are used in the estimation process. The mathematical model, as well as the initial condition, are supposed to have some error, which are taken into account in the probabilistic evolution model used for the filter.

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Yousefian, Sajjad, Gilles Bourque, Sandeep Jella, Philippe Versailles i Rory F. D. Monaghan. "A Stochastic and Bayesian Inference Toolchain for Uncertainty and Risk Quantification of Rare Autoignition Events in DLE Premixers". W ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83667.

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Abstract Quantification of aleatoric uncertainties due to the inherent variabilities in operating conditions and fuel composition is essential for designing and improving premixers in dry low-emissions (DLE) combustion systems. Advanced stochastic simulation tools require a large number of evaluations in order to perform this type of uncertainty quantification (UQ) analysis. This task is computationally prohibitive using high-fidelity computational fluid dynamic (CFD) approaches such as large eddy simulation (LES). In this paper, we describe a novel and computationally-efficient toolchain for stochastic modelling using minimal input from LES, to perform uncertainty and risk quantification of a DLE system. More specially, high-fidelity LES, chemical reactor network (CRN) model, beta mixture model, Bayesian inference and sequential Monte Carlo (SMC) are integrated into the toolchain. The methodology is applied to a practical premixer of low-emission combustion system with dimethyl ether (DME)/methane-air mixtures to simulate autoignition events at different engine conditions. First, the benchmark premixer is simulated using a set of LESs for a methane/air mixture at elevated pressure and temperature conditions. A partitioning approach is employed to generate a set of deterministic chemical reactor network (CRN) models from LES results. These CRN models are then solved at the volume-average conditions and validated by LES results. A mixture modelling approach using the expectation-method of moment (EMM) is carried out to generate a set of beta mixture models and characterise uncertainties for LES-predicted temperature distributions. These beta mixture models and a normal distribution for DME volume fraction are used to simulate a set of stochastic CRN models. The Bayesian inference approach through Sequential Monte Carlo (SMC) method is then implemented on the results of temperature distributions from stochastic CRN models to simulate the probability of autoignition in the benchmark premixer. The results present a very satisfactory performance for the stochastic toolchain to compute the autoignition propensity for a few events with a particular combination of inlet temperature and DME volume fraction. Characterisation of these rare events is computationally prohibitive in the conventional deterministic methods such as high-fidelity LES.

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Lee, Jae-young, Shahram Payandeh i Ljiljana Trajkovic´. "The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method". W ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40495.

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In this paper, we present motion and force predictions in Internet-based teleoperation systems using the particle filter method. The particle filter, also known as the sequential Monte Carlo (SMC) method, is a probabilistic prediction or estimation technique within a sequential Bayesian framework: Data at a current time step are predicted or estimated by recursively generating probability distribution based on previous observations and input states. In this paper, we first formulate the particle filter method using a prediction-based approach. Motion and force data flows, which may be impaired by the Internet delay, are formulated within a sequential Bayesian framework. The true motion and force data are then predicted by employing the prediction-based particle filter method using the impaired observations and previous input states. We performed experiments using a haptic device that interacts with a mechanics-based virtual 3D graphical environment. The haptic device is used as a master controller that provides positioning inputs to a 4-degree of freedom (4-DoF) virtual robotic manipulator while receiving feedback force through interactions with the virtual environment. We simulate the Internet delay with variations typically observed in a user datagram protocol (UDP) transmission between the master controller and the virtual teleoperated robot. In this experimental scenario, the particle filter method is implemented for both motion and force data that experience the Internet delay. The proposed method is compared with the conventional Kalman filter. Experimental results indicate that in nonlinear and non-Gaussian environments the prediction-based particle filter has distinct advantage over other methods.

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Gao, Hongzhi, i Richard Green. "A sequential Monte Carlo method for particle filters". W 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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Wen, Quan, i Jean Gao. "Tracking Interacting Subcellular Structures By Sequential Monte Carlo Method". W 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353259.

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Mancasi, Monica, i Ramona Vatu. "Smart grids reliability indices assessment using sequential Monte Carlo method". W 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC). IEEE, 2015. http://dx.doi.org/10.1109/eeeic.2015.7165495.

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Wen, Quan, Jean Gao i Kate Luby-Phelps. "Multiple Interacting Subcellular Structure Tracking by Sequential Monte Carlo Method". W 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/bibm.2007.28.

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Tan, Hui, Xinmeng Chen i Min Jiang. "Object Tracking based on Snake and Sequential Monte Carlo Method". W Sixth International Conference on Intelligent Systems Design and Applications. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.253863.

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Huda, A. S. N., i Rastko Zivanovic. "Distribution system reliability assessment using sequential multilevel Monte Carlo method". W 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). IEEE, 2016. http://dx.doi.org/10.1109/isgt-asia.2016.7796499.

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Ermolaev, Petr A., Maxim A. Volynsky i Pavel A. Skakov. "Evaluation of interference fringe parameters using sequential Monte Carlo method". W SPIE Optical Metrology, redaktorzy Peter Lehmann, Wolfgang Osten i Armando Albertazzi Gonçalves. SPIE, 2015. http://dx.doi.org/10.1117/12.2184578.

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Raporty organizacyjne na temat "Sequential Monte Carlo (SMC) method"

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

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