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22
RaoBlackwellized Particle Filter for Multiple Target Tracking
 Information Fusion Journal
, 2005
"... In this article we propose a new RaoBlackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these st ..."
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Cited by 53 (4 self)
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In this article we propose a new RaoBlackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle filtering, and the e#ciency of the Monte Carlo sampling is improved by using RaoBlackwellization.
Reversible Jump MCMC Simulated Annealing for Neural Networks
"... We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global ..."
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Cited by 17 (2 self)
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We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We also show that by calibrating a Bayesian model, we can obtain the classical AIC, BIC and MDL model selection criteria within a penalized likelihood framework. Finally, we show theoretically and empirically that the algorithm converges to the modes of the full posterior distribution in an efficient way. likelihood estimation, with the aforementioned model selection criteria, is performed by maximizing the calibrated posterior distribution. To accomplish this goal, we propose an MCMC simulated annealing algorithm, which makes use of a homogeneous reversible jump MCMC kernel as proposal. This approach has the advantage that we can start with an arbitrary model order and the algorithm will perform dimension jumps until it finds the "true " model order. That is, one does not have to resort to the more expensive task of running a fixed dimension algorithm for each possible model order and subsequently selecting the best model. We also present a convergence theorem for the algorithm. The complexity of the problem does not allow for a comprehensive discussion in this short paper.
Efficient Approximate Inference for Online Probabilistic Plan Recognition
 AAAI FALL SYMPOSIUM ON INTENT INFERENCE FOR USERS, TEAMS AND ADVERSARIES
, 2002
"... We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We ..."
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Cited by 9 (3 self)
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We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHMEM can represent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHMEM based on the RaoBlackwellised Particle Filter approximate inference method.
A RaoBlackwellized particle filter for magnetoencephalography
 Inverse Problems
"... A Rao–Blackwellized particle filter for the tracking of neural sources from biomagnetic data is described. A comparison with a sampling importance resampling particle filter performed in the case of both simulated and real data shows that the use of Rao–Blackwellization is highly recommended since i ..."
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Cited by 8 (6 self)
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A Rao–Blackwellized particle filter for the tracking of neural sources from biomagnetic data is described. A comparison with a sampling importance resampling particle filter performed in the case of both simulated and real data shows that the use of Rao–Blackwellization is highly recommended since it produces more accurate reconstructions within a lower computational effort. 1.
UAI 2004 HAMZE & FREITAS 243 From Fields to Trees
"... We present new MCMC algorithms for computing the posterior distributions and expectations of the unknown variables in undirected graphical models with regular structure. For demonstration purposes, we focus on Markov Random Fields (MRFs). By partitioning the MRFs into nonoverlapping trees, it is po ..."
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We present new MCMC algorithms for computing the posterior distributions and expectations of the unknown variables in undirected graphical models with regular structure. For demonstration purposes, we focus on Markov Random Fields (MRFs). By partitioning the MRFs into nonoverlapping trees, it is possible to compute the posterior distribution of a particular tree exactly by conditioning on the remaining tree. These exact solutions allow us to construct efficient blocked and RaoBlackwellised MCMC algorithms. We show empirically that tree sampling is considerably more efficient than other partitioned sampling schemes and the naive Gibbs sampler, even in cases where loopy belief propagation fails to converge. We prove that tree sampling exhibits lower variance than the naive Gibbs sampler and other naive partitioning schemes using the theoretical measure of maximal correlation. We also construct new information theory tools for comparing different MCMC schemes and show that, under these, tree sampling is more efficient. 1
Comparative Study of Algorithms for Frontier based Area Exploration and Slam for Mobile Robots
"... Exploration strategies are used to guide mobile robots for map building. Usually, exploration strategies work greedily by evaluating a number of candidate observations on the basis of a utility function and selecting the best one. The core challenge in area exploration is to deploy a large number of ..."
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Exploration strategies are used to guide mobile robots for map building. Usually, exploration strategies work greedily by evaluating a number of candidate observations on the basis of a utility function and selecting the best one. The core challenge in area exploration is to deploy a large number of robots in an unknown environment, map the environment and establishing an efficient communication between the robots. Simultaneous Localization and Mapping (SLAM) comes in to add more accuracy and heuristics to the generic area exploration strategies. Addition to SLAM algorithms will improve the performance of the exploration process and map building to a great extend. In this paper a survey of existing approaches in frontier based area exploration and various SLAM algorithms which can be useful for the process of area exploration are discussed.
A Collapsed Variational Bayesian InferenceAlgorithm for Latent Dirichlet Allocation
"... Abstract Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gainedmuch popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibb sampling have ..."
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Abstract Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gainedmuch popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibb sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA,and show that it is computationally efficient, easy to implement and significantly more accurate than standard variational Bayesian inference for LDA.
A Collapsed Variational Bayesian InferenceAlgorithm for Latent Dirichlet Allocation
"... Abstract Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gainedmuch popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling hav ..."
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Abstract Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gainedmuch popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm forLDA, and show that it is computationally efficient, easy to implement and significantly more accurate than standard variational Bayesian inference for LDA.
Improved State Estimation in Multiagent Settings with Continuous or Large Discrete State Spaces
"... State estimation in multiagent settings involves updating an agent’s belief over the physical states and the space of other agents ’ models. Performance of the previous approach to state estimation, the interactive particle filter, degrades with large state spaces because it distributes the particle ..."
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State estimation in multiagent settings involves updating an agent’s belief over the physical states and the space of other agents ’ models. Performance of the previous approach to state estimation, the interactive particle filter, degrades with large state spaces because it distributes the particles over both, the physical state space and the other agents ’ models. We present an improved method for estimating the state in a class of multiagent settings that are characterized in part by continuous or large discrete state spaces. We factor out the models of the other agents and update the agent’s belief over these models, as exactly as possible. Simultaneously, we sample particles from the distribution over the large physical state space and project the particles in time. This approach is equivalent to RaoBlackwellising the interactive particle filter. We focus our analysis on the special class of problems where the nested beliefs are represented using Gaussians, the problem dynamics using conditional linear Gaussians (CLGs) and the observation functions using softmax or CLGs. These distributions adequately represent many realistic applications.
From Fields to Trees
 In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI04
, 2004
"... We present new MCMC algorithms for computing the posterior distributions and expectations of the unknown variables in undirected graphical models with regular structure. ..."
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We present new MCMC algorithms for computing the posterior distributions and expectations of the unknown variables in undirected graphical models with regular structure.