The adjoint process for a partially observed Markov chain

1990 ◽  
Author(s):  
R.J. Elliott
Keyword(s):  
Markov Chain ◽  
Partially Observed

scholarly journals A uniformly convergent adaptive particle filter

2005 ◽  
Vol 42 (4) ◽  
pp. 1053-1068 ◽  
Author(s):  
Anastasia Papavasiliou
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Particle Filter ◽  
Adaptive Estimation ◽  
Optimal Filter ◽  
Simultaneous Estimation ◽  
Unknown Parameters ◽  
Partially Observed ◽  
Uniformly Convergent ◽  
Number Of Particles

Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.

scholarly journals Analysis of an adaptive control scheme for a partially observed controlled Markov chain

1993 ◽  
Vol 38 (6) ◽  
pp. 987-993 ◽  
Author(s):  
E. Fernandez-Gaucherand ◽  
A. Arapostathis ◽  
S.I. Marcus
Keyword(s):  
Adaptive Control ◽  
Markov Chain ◽  
Control Scheme ◽  
Partially Observed ◽  
Adaptive Control Scheme

Robust Dynamics and Control of a Partially Observed Markov Chain

2007 ◽  
Vol 56 (3) ◽  
pp. 303-311 ◽  
Author(s):  
R. J. Elliott ◽  
W. P. Malcolm ◽  
J. P. Moore
Keyword(s):  
Markov Chain ◽  
Partially Observed ◽  
Dynamics And Control ◽  
And Control

scholarly journals On Preventing Location Attacks for Urban Vehicular Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Zhou ◽  
Xin Li ◽  
Lejian Liao
Keyword(s):  
Markov Chain ◽  
Vehicular Networks ◽  
Location Privacy ◽  
Location Based Services ◽  
Experimental Results ◽  
Order Markov Chain ◽  
Proposed Model ◽  
Partially Observed ◽  
Higher Order Markov Chain ◽  
Bayesian Personalized Ranking

The prevalence of global positioning system (GPS) equipped in vehicular networks exposes users’ location information to the location-based services. We argue that such data contains rich informative cues on drivers’ private behaviors and preferences, which will lead to the location privacy attacks. In this paper, we proposed a sophisticated prediction model to predict driver’s next location by using ak-order Markov chain-based third-rank tensor representing the partially observed transfer information of vehicles. Then Bayesian Personalized Ranking (BPR) is used to learn the unobserved transitions within the tensor for transition predication. Experimental results manifest the efficacy of the proposed model in terms of location predication accuracy, compared with several state-of-the-art predication methods. We also point out that the precision achieved by such advanced predication model is restricted to the order of the Markov chaink. Accordingly, we propose a schema to decrease the risks of such attacks by preventing the conformation of higher order Markov chain. Experimental results obtained based on the real-world vehicular network data demonstrated the effectiveness of our proposed schema.

scholarly journals A uniformly convergent adaptive particle filter

2005 ◽  
Vol 42 (04) ◽  
pp. 1053-1068 ◽  
Author(s):  
Anastasia Papavasiliou
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Particle Filter ◽  
Adaptive Estimation ◽  
Optimal Filter ◽  
Simultaneous Estimation ◽  
Unknown Parameters ◽  
Partially Observed ◽  
Uniformly Convergent ◽  
Number Of Particles

Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.

scholarly journals On adaptive control of a partially observed Markov chain

1994 ◽  
Vol 22 (2) ◽  
pp. 165-180 ◽  
Author(s):  
Giovanni Di Masi ◽  
Łukasz Stettner
Keyword(s):  
Adaptive Control ◽  
Markov Chain ◽  
Partially Observed
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