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Subject Item: dbr:Ensemble_Kalman_filter
dbo:wikiPageWikiLink: dbr:Particle_filter

Subject Item: dbr:Metropolis–Hastings_algorithm
dbo:wikiPageWikiLink: dbr:Particle_filter

Subject Item: dbr:Monte_Carlo_integration
dbo:wikiPageWikiLink: dbr:Particle_filter

Subject Item: dbr:Monte_Carlo_POMDP
dbo:wikiPageWikiLink: dbr:Particle_filter

Subject Item: dbr:Particle_filter
rdf:type: yago:Whole100003553 yago:Filter103339643 yago:PhysicalEntity100001930 yago:Instrumentality103575240 yago:WikicatNonlinearFilters yago:Artifact100021939 yago:Device103183080 yago:Object100002684
rdfs:label: Многочастичный фильтр Particle filter 粒子フィルタ Sequenzielle Monte-Carlo-Methode Filtre particulaire Filtr cząsteczkowy 파티클 필터 Filtro de partículas 粒子濾波器
rdfs:comment: Sequenzielle Monte-Carlo-Methoden (SMC-Methoden) gehören zur Klasse der stochastischen Verfahren zur Zustandsschätzung in einem dynamischen Prozess (z. B. in der mobilen Robotik), dessen Dynamik nur im statistischen Mittel bekannt ist (wesentliche Störgrößen) und der nur unvollständig beobachtet werden kann (Unterteilung in innere, verborgene und äußere, sichtbare Variable). Ein Anwendungsbeispiel ist die genaue und kontinuierlich aktualisierte Bestimmung des Ortes und der Geschwindigkeit eines Objektes aufgrund einer ungenauen und fehlerhaften Messung des Ortes (vgl. Tracking). SMC-Filter sind auch bekannt als Partikel-Filter, sampling importance resampling (SIR), sequential importance sampling (SIS), bootstrap filters, condensation trackers, interacting particle approximations oder survi Les filtres particulaires, aussi connus sous le nom de méthodes de Monte-Carlo séquentielles, sont des techniques sophistiquées d'estimation de modèles fondées sur la simulation. Les filtres particulaires sont généralement utilisés pour estimer des réseaux bayésiens et constituent des méthodes 'en-ligne' analogues aux méthodes de Monte-Carlo par chaînes de Markov qui elles sont des méthodes 'hors-ligne' (donc a posteriori) et souvent similaires aux méthodes d'échantillonnage préférentiel. 粒子フィルタ（りゅうしフィルタ、英: particle filter）や逐次モンテカルロ法 (ちくじモンテカルロほう、英: sequential Monte Carlo; SMC)とは、シミュレーションに基づく複雑なモデルの推定法である。1993年1月に北川源四郎がモンテカルロフィルタの名称で、1993年4月にN.J. Gordonらがブートストラップフィルタの名称で同時期に同じものを発表した。この手法はふつうベイズモデルを推定するのに用いられ、バッチ処理であるマルコフ連鎖モンテカルロ法 (MCMC) の逐次 (オンライン) 版である。またこの手法は法にも似たところがある。うまく設計すると、粒子フィルタはMCMCよりも高速である。拡張カルマンフィルタや無香カルマンフィルタ (Unscented カルマンフィルタ) に比べて、サンプル点が十分多くなるとベイズ最適推定に近付くことからより高い精度の解が得られるので、これらの代わりに用いられることがある。また手法を組み合わせて、カルマンフィルタを粒子フィルタの提案分布として使うこともできる。 Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s. The term "Sequential Monte Carlo" was coined by Liu and Chen in 1998. Многочасти́чный фильтр (МЧФ, англ. particle filter — «фильтр частиц», «частичный фильтр», «корпускулярный фильтр») — последовательный метод Монте-Карло — рекурсивный алгоритм для численного решения проблем оценивания (фильтрации, сглаживания), особенно для нелинейных и не-гауссовских случаев. Со времени описания в 1993 году Н. Гордоном, Д. Салмондом и А. Смитом используется в различных областях — навигации, робототехнике, компьютерном зрении. Термин «particle filter» был дан Дел Моралом в 1996 году, а «sequential Monte Carlo» — Лю (Liu) и Ченом (Chen) в 1998. El "filtro de partículas" es un método empleado para estimar el estado de un sistema que cambia a lo largo del tiempo. Más concretamente, es un método de Montecarlo (secuencial) usado comúnmente en visión artificial para el seguimiento de objetos en secuencias de imágenes. Posee cuatro etapas principales: * Inicialización. * Actualización. * Estimación. * Predicción. 粒子滤波器（英語：particle filter）是一种使用蒙特卡罗方法的递归滤波器，透过一组具有权重的随机样本（粒子）來表示隨機事件的後驗機率，從含有雜訊或不完整的觀測序列，估計出動態系統的狀態，粒子濾波器可以運用在任何狀態空間的模型上。粒子濾波器是卡爾曼濾波器的一般化方法，卡爾曼濾波器建立在線性的狀態空間和高斯分布的雜訊上；而粒子濾波器的狀態空間模型可以是非線性，且雜訊分布可以是任何型式。 Filtr cząsteczkowy (sekwencyjna metoda Monte Carlo, SMC) – metoda nieliniowej filtracji, polegająca na oszacowaniu rozkładów prawdopodobieństwa docelowego przez rozkłady empiryczne, skupione na zestawie próbek zwanych cząsteczkami. Próbki wyznaczane są przez algorytm sekwencyjny, łączący metody losowania istotnego (ang. importance sampling) z technikami ponownego próbkowania (ang. resampling). Nazwę filtr cząsteczkowy zaproponował , natomiast sekwencyjną metodę Monte Carlo i , oba terminy używane są zamiennie. 파티클 필터(영어: Particle filter)는 시뮬레이션에 기반을 둔 예측기술의 하나로 계속적인 몬테카를로 방법이라고도 한다. 파티클 필터는 계량경제학에서 중요하게 쓰인다. 파티클 필터는 보통 베이즈 모델을 추정하기 위해 사용된다. 이는 잠재변수가 마르코프 연쇄로 서로 관련되어 있는 경우로 은닉 마르코프 모델(HMM)과 비슷하지만 보통 드러나지 않은 변수의 상태 공간이 연속적이고, 정확하게 추정할 수 있을 만큼 한정적이지 않다. 예를 들어, 선형 동적 시스템에서는, 잠재변수의 상태 공간이 가우스 분포에 한정되는데, 따라서 정확한 추정이 효과적으로 칼만 필터만으로 이루어질 수 있다. HMM, 그리고 관련된 모델의 맥락에서 보면, 필터링은 어떤 특정 시간에 잠재변수의 분포를 결정하되, 관측 값은 그 시간 까지만 주어진다; 파티클 필터라는 이름을 얻게 된 까닭은 근사값을 (아까 말한 의미에서) "필터링"하는데 한 무리의 (다른 가중치를 가진 분포의 예) "입자"를 사용하기 때문이다.
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dbo:abstract: 파티클 필터(영어: Particle filter)는 시뮬레이션에 기반을 둔 예측기술의 하나로 계속적인 몬테카를로 방법이라고도 한다. 파티클 필터는 계량경제학에서 중요하게 쓰인다. 파티클 필터는 보통 베이즈 모델을 추정하기 위해 사용된다. 이는 잠재변수가 마르코프 연쇄로 서로 관련되어 있는 경우로 은닉 마르코프 모델(HMM)과 비슷하지만 보통 드러나지 않은 변수의 상태 공간이 연속적이고, 정확하게 추정할 수 있을 만큼 한정적이지 않다. 예를 들어, 선형 동적 시스템에서는, 잠재변수의 상태 공간이 가우스 분포에 한정되는데, 따라서 정확한 추정이 효과적으로 칼만 필터만으로 이루어질 수 있다. HMM, 그리고 관련된 모델의 맥락에서 보면, 필터링은 어떤 특정 시간에 잠재변수의 분포를 결정하되, 관측 값은 그 시간 까지만 주어진다; 파티클 필터라는 이름을 얻게 된 까닭은 근사값을 (아까 말한 의미에서) "필터링"하는데 한 무리의 (다른 가중치를 가진 분포의 예) "입자"를 사용하기 때문이다. 파티클 필터는 마르코프 연쇄 몬테 카를로(MCMC) 일괄처리법을 으로 유사하게 만든 것으로 때로 과 유사하다. 입자 필터를 잘 만들면 MCMC보다 훨씬 빠르다. 때때로 확장 칼만 필터(EKF) 또는 무향 칼만 필터(UKF) 대신 사용된다. EKF나 UKF에 비해 잇점은, 샘플이 충분하다면, 배이지언 최적 추정치에 접근하므로 EKF나 UKF보다 정확하다. 그러나, 샘플의 수가 충분하지 않다면 문제가 생길 수 있다. 파티클 필터와 칼만 필터를 복합하여 사용할 수도 있는데, 일종의 칼만 필터를 사용하여 파티클 필터를 위한 분포를 제안하게 하는 방식이다. Filtr cząsteczkowy (sekwencyjna metoda Monte Carlo, SMC) – metoda nieliniowej filtracji, polegająca na oszacowaniu rozkładów prawdopodobieństwa docelowego przez rozkłady empiryczne, skupione na zestawie próbek zwanych cząsteczkami. Próbki wyznaczane są przez algorytm sekwencyjny, łączący metody losowania istotnego (ang. importance sampling) z technikami ponownego próbkowania (ang. resampling). Nazwę filtr cząsteczkowy zaproponował , natomiast sekwencyjną metodę Monte Carlo i , oba terminy używane są zamiennie. Sequenzielle Monte-Carlo-Methoden (SMC-Methoden) gehören zur Klasse der stochastischen Verfahren zur Zustandsschätzung in einem dynamischen Prozess (z. B. in der mobilen Robotik), dessen Dynamik nur im statistischen Mittel bekannt ist (wesentliche Störgrößen) und der nur unvollständig beobachtet werden kann (Unterteilung in innere, verborgene und äußere, sichtbare Variable). Ein Anwendungsbeispiel ist die genaue und kontinuierlich aktualisierte Bestimmung des Ortes und der Geschwindigkeit eines Objektes aufgrund einer ungenauen und fehlerhaften Messung des Ortes (vgl. Tracking). SMC-Filter sind auch bekannt als Partikel-Filter, sampling importance resampling (SIR), sequential importance sampling (SIS), bootstrap filters, condensation trackers, interacting particle approximations oder survival of the fittest. 粒子滤波器（英語：particle filter）是一种使用蒙特卡罗方法的递归滤波器，透过一组具有权重的随机样本（粒子）來表示隨機事件的後驗機率，從含有雜訊或不完整的觀測序列，估計出動態系統的狀態，粒子濾波器可以運用在任何狀態空間的模型上。粒子濾波器是卡爾曼濾波器的一般化方法，卡爾曼濾波器建立在線性的狀態空間和高斯分布的雜訊上；而粒子濾波器的狀態空間模型可以是非線性，且雜訊分布可以是任何型式。 El "filtro de partículas" es un método empleado para estimar el estado de un sistema que cambia a lo largo del tiempo. Más concretamente, es un método de Montecarlo (secuencial) usado comúnmente en visión artificial para el seguimiento de objetos en secuencias de imágenes. Fue propuesto en 1993 por , y como filtro bootstrap para implementar filtros bayesianos recursivos. Básicamente, el filtro de partículas se compone de un conjunto de muestras (las partículas) y unos valores, o pesos, asociados a cada una de esas muestras. Las partículas son estados posibles del proceso, que se pueden representar como puntos en el espacio de estados de dicho proceso. Posee cuatro etapas principales: * Inicialización. * Actualización. * Estimación. * Predicción. Para realizar el seguimiento de un objeto sobre una secuencia de imágenes, el filtro de partículas "lanza" al azar un conjunto de puntos sobre la imagen (etapa de inicialización, se crea un conjunto de partículas con un estado aleatorio), realizando cálculos se le asignará un valor, o valores, a cada uno de esos puntos (etapa de actualización). A partir de estos valores, se creará un nuevo conjunto de puntos que reemplazará al anterior. Esta elección también será al azar, pero los valores que se han adjudicado a cada uno de los puntos provocarán que sea más probable de elegir aquellos puntos que hayan capturado al objeto sobre el que quiere realizar el seguimiento (etapa de estimación). Una vez que se crea el nuevo conjunto de puntos, se realiza una leve modificación al estado (posición) de cada uno de ellos, con el fin de estimar el estado del objeto en el instante siguiente (etapa de predicción). Al terminar la etapa de predicción, se obtiene un nuevo conjunto de puntos al que se le vuelve a aplicar la etapa de actualización, repitiéndose este bucle hasta que termine la secuencia o desaparezca el objeto, caso en el cual se volvería a la etapa de inicialización. Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s. The term "Sequential Monte Carlo" was coined by Liu and Chen in 1998. Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of a stochastic process given the noisy and/or partial observations. The state-space model can be nonlinear and the initial state and noise distributions can take any form required. Particle filter techniques provide a well-established methodology for generating samples from the required distribution without requiring assumptions about the state-space model or the state distributions. However, these methods do not perform well when applied to very high-dimensional systems. Particle filters update their prediction in an approximate (statistical) manner. The samples from the distribution are represented by a set of particles; each particle has a likelihood weight assigned to it that represents the probability of that particle being sampled from the probability density function. Weight disparity leading to weight collapse is a common issue encountered in these filtering algorithms, however, it can be mitigated by including a resampling step before the weights become uneven. Several adaptive resampling criteria can be used including the variance of the weights and the relative entropy concerning the uniform distribution. In the resampling step, the particles with negligible weights are replaced by the new particles in the proximity of the particles with higher weights. From the statistical and probabilistic point of view, particle filters may be interpreted as mean-field particle interpretations of Feynman-Kac probability measures. These particle integration techniques were developed in molecular chemistry and computational physics by Theodore E. Harris and Herman Kahn in 1951, Marshall N. Rosenbluth and Arianna W. Rosenbluth in 1955, and more recently by Jack H. Hetherington in 1984. In computational physics, these Feynman-Kac type path particle integration methods are also used in Quantum Monte Carlo, and more specifically Diffusion Monte Carlo methods. Feynman-Kac interacting particle methods are also strongly related to mutation-selection genetic algorithms currently used in evolutionary computing to solve complex optimization problems. The particle filter methodology is used to solve Hidden Markov Model (HMM) and nonlinear filtering problems. With the notable exception of linear-Gaussian signal-observation models (Kalman filter) or wider classes of models (Benes filter), Mireille Chaleyat-Maurel and Dominique Michel proved in 1984 that the sequence of posterior distributions of the random states of a signal, given the observations (a.k.a. optimal filter), has no finite recursion. Various other numerical methods based on fixed grid approximations, Markov Chain Monte Carlo techniques, conventional linearization, extended Kalman filters, or determining the best linear system (in the expected cost-error sense) are unable to cope with large-scale systems, unstable processes, or when the nonlinearities are not sufficiently smooth. Particle filters and Feynman-Kac particle methodologies find application in signal and image processing, Bayesian inference, machine learning, risk analysis and rare event sampling, engineering and robotics, artificial intelligence, bioinformatics, phylogenetics, computational science, economics and mathematical finance, molecular chemistry, computational physics, pharmacokinetics, and other fields. 粒子フィルタ（りゅうしフィルタ、英: particle filter）や逐次モンテカルロ法 (ちくじモンテカルロほう、英: sequential Monte Carlo; SMC)とは、シミュレーションに基づく複雑なモデルの推定法である。1993年1月に北川源四郎がモンテカルロフィルタの名称で、1993年4月にN.J. Gordonらがブートストラップフィルタの名称で同時期に同じものを発表した。この手法はふつうベイズモデルを推定するのに用いられ、バッチ処理であるマルコフ連鎖モンテカルロ法 (MCMC) の逐次 (オンライン) 版である。またこの手法は法にも似たところがある。うまく設計すると、粒子フィルタはMCMCよりも高速である。拡張カルマンフィルタや無香カルマンフィルタ (Unscented カルマンフィルタ) に比べて、サンプル点が十分多くなるとベイズ最適推定に近付くことからより高い精度の解が得られるので、これらの代わりに用いられることがある。また手法を組み合わせて、カルマンフィルタを粒子フィルタの提案分布として使うこともできる。 Многочасти́чный фильтр (МЧФ, англ. particle filter — «фильтр частиц», «частичный фильтр», «корпускулярный фильтр») — последовательный метод Монте-Карло — рекурсивный алгоритм для численного решения проблем оценивания (фильтрации, сглаживания), особенно для нелинейных и не-гауссовских случаев. Со времени описания в 1993 году Н. Гордоном, Д. Салмондом и А. Смитом используется в различных областях — навигации, робототехнике, компьютерном зрении. В сравнении с обычно применяемыми для подобных задач методами — расширенными фильтрами Кальмана (EKF) — многочастичные фильтры не зависят от методов линеаризации или апроксимации. Обычный EKF плохо справляется с существенно нелинейными моделями, а также в случае шумов системы и измерений, сильно отличающихся от гауссовых, поэтому были разработаны различные модификации, такие как UKF (англ. unscented KF), QKF (англ. Quadrature KF) и т. п.. Следует отметить, что в свою очередь многочастичные фильтры более требовательны к вычислительным ресурсам. Термин «particle filter» был дан Дел Моралом в 1996 году, а «sequential Monte Carlo» — Лю (Liu) и Ченом (Chen) в 1998. Многие используемые на практике многочастичные фильтры выводятся применением последовательного метода Монте-Карло к последовательности целевых распределений. Les filtres particulaires, aussi connus sous le nom de méthodes de Monte-Carlo séquentielles, sont des techniques sophistiquées d'estimation de modèles fondées sur la simulation. Les filtres particulaires sont généralement utilisés pour estimer des réseaux bayésiens et constituent des méthodes 'en-ligne' analogues aux méthodes de Monte-Carlo par chaînes de Markov qui elles sont des méthodes 'hors-ligne' (donc a posteriori) et souvent similaires aux méthodes d'échantillonnage préférentiel. S'ils sont conçus correctement, les filtres particulaires peuvent être plus rapides que les méthodes de Monte-Carlo par chaînes de Markov. Ils constituent souvent une alternative aux filtres de Kalman étendus avec l'avantage qu'avec suffisamment d'échantillons, ils approchent l'estimé Bayésien optimal. Ils peuvent donc être rendus plus précis que les filtres de Kalman. Les approches peuvent aussi être combinées en utilisant un filtre de Kalman comme une proposition de distribution pour le filtre particulaire.
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