Backward Simulation Methods for Monte Carlo Statistical Inference

Lindsten, Fredrik

doi:10.1561/9781601986993

Cited by 58 publications

(103 citation statements)

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“…Each particle corresponds to an entire trajectory X [0,T ] . Among the various SMC methods for smoothing, one can distinguish broadly speaking three approaches; first, the bootstrap Filter-Smoother (FS) by [5], second, the forwardbackward smoothers [6], [7]-with its many variations [8]and third, the two-filter smoothers [9], [10], [11]. All these methods have their particular strengths and weaknesses.…”

Section: A Particle Filtering Methodsmentioning

confidence: 99%

“…For instance, in [17] a rejection sampling approach was suggested to avoid the computational complexity of evaluating all backward weights, effectively reducing the overall computational complexity to O(N ) provided that N is sufficiently large. However in practice, this approach is less efficient than FFBSi for many problems and does not scale to high dimensions [8].…”

Section: A Particle Filtering Methodsmentioning

confidence: 99%

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Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother

Ruiz

Kappen

2017

IEEE Trans. Signal Process.

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Smoothing methods are used for inference of stochastic processes given noisy observations. The estimation of the marginal posterior distribution given all observations is typically a computationally intensive task. We propose a novel algorithm based on path integral control theory to efficiently estimate the smoothing distribution of continuous-time diffusion processes from partial observations. In particular, we use an adaptive importance sampling method to improve the effective sampling size of the posterior and the reliability of the estimation of the marginals. This is achieved by estimating a feedback controller to help sample efficiently from the joint smoothing distribution. We compare the results with estimations obtained from the standard Forward Filter/Backward Simulator (FFBSi) for two diffusion processes of different complexity. We show that the proposed method gives more accurate estimates than the standard FFBSi.

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Section: A Particle Filtering Methodsmentioning

confidence: 99%

Section: A Particle Filtering Methodsmentioning

confidence: 99%

Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother

Ruiz

Kappen

2017

IEEE Trans. Signal Process.

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“…In vector based state space models, it is often important to consider the posterior density over the state trajectory, which contains the states at all time steps, i.e., it is not sufficient to merely find all the marginal densities of the states at all times [31], [32]. For example, the posterior density over the trajectory is necessary to calculate the maximum a posterior (MAP) estimator of the trajectory [33] or to answer trajectoryrelated questions, e.g., what is the probability that the state was in a region at a time and has moved to another region at a different time step?…”

Section: B Motivation For Sets Of Trajectoriesmentioning

confidence: 99%

Multiple Target Tracking Based on Sets of Trajectories

García-Fernández

Svensson

Morelande

2020

IEEE Trans. Aerosp. Electron. Syst.

101

112

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We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.

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“…In this way, the resulting Markov kernel leaves its target distribution invariant, regardless of the number of particles used. In contrast to other particle Gibbs with backward simulation methods [47], [48], PGAS can also be applied to non-Markovian latent variable models, i.e., models that are not expressed on a state-space form [31], [49]. In this section, we briefly describe the PGAS algorithm and provide the necessary equations for its implementation.…”

Section: A Particle Gibbs With Ancestor Samplingmentioning

confidence: 99%

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

Ruiz

Valera

Svensson

et al. 2018

IEEE Trans. Cogn. Commun. Netw.

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New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the datagenerating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.

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Backward Simulation Methods for Monte Carlo Statistical Inference

Cited by 58 publications

References 0 publications

Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother

Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother

Multiple Target Tracking Based on Sets of Trajectories

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

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