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Bayes linear kinematics in a dynamic Bayesian survival model [article]

Kevin J. Wilson, Malcolm Farrow
2016 arXiv   pre-print
Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment.  ...  In this paper we investigate the application of this approach to a more complicated problem: namely survival analysis with time-dependent covariate effects.  ...  as in a dynamic linear model.  ... 
arXiv:1411.2497v2 fatcat:j6uvmoevrvc33gqyjr52s36x7q

Bayes linear kinematics in a dynamic survival model

Kevin J. Wilson, Malcolm Farrow
2017 International Journal of Approximate Reasoning  
Bayes linear kinematics in a dynamic survival model.  ...  Abstract Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment  ...  as in a dynamic linear model.  ... 
doi:10.1016/j.ijar.2016.09.010 fatcat:vltmqdzksraufpgxt7dgwfkc7a

Bayes linear kinematics in the analysis of failure rates and failure time distributions

K J Wilson, M Farrow, Frank Coolen, Michael Oberguggenberger, Matthias Troffaes
2010 Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability  
An alternative approach using Bayes linear kinematics [1] in which simple conjugate specifications for individual counts are linked through a Bayes linear belief structure is presented.  ...  A conventional Bayesian analysis requires a rather indirect prior specification and intensive numerical methods for posterior evaluations.  ...  Acknowledgement We are grateful to two referees and a guest editor for helpful comments.  ... 
doi:10.1243/1748006xjrr293 fatcat:jpyo6e75c5a6fmkkll6mwm5n5y

A Generalized Labeled Multi-Bernoulli Filter for Maneuvering Targets [article]

Yuthika Punchihewa, Ba-Ngu Vo, Ba-Tuong Vo
2016 arXiv   pre-print
A multiple maneuvering target system can be viewed as a Jump Markov System (JMS) in the sense that the target movement can be modeled using different motion models where the transition between the motion  ...  models by a particular target follows a Markov chain probability rule.  ...  The new state of a surviving target will also be governed by the probability of the target transitioning to that motion model from the previous model in addition to the probability of survival and the  ... 
arXiv:1603.04565v1 fatcat:q4vgqznwbjfq5bwgfzyswf6zui

A random finite set conjugate prior and application to multi-target tracking

Ba-Tuong Vo, Ba-Ngu Vo
2011 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing  
This result is then applied to develop an analytic implementation of the Bayes multi-target filter for the class of linear Gaussian multi-target models.  ...  This estimation problem can be formulated in a Bayesian framework by modeling the (hidden) set of states and set of observations as random finite sets (RFSs) where the model for the observation covers  ...  This result is applied to develop an analytic implementation of the Bayes multi-target filter for linear Gaussian multi-target models. II.  ... 
doi:10.1109/issnip.2011.6146549 fatcat:dzyixxfcnbcatijkvb7ue27fce

A Gaussian Mixture PHD Filter for Jump Markov System Models

Syed Ahmed Pasha, Ba-Ngu Vo, Hoang Duong Tuan, Wing-Kin Ma
2009 IEEE Transactions on Aerospace and Electronic Systems  
In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths and switching dynamics.  ...  The PHD filter admits a closed form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models.  ...  Model r = 1 is a co-ordinated turn model with a turn rate of 0 • s −1 with linear Gaussian dynamics Perturbations in the lift and drag characteristics due to changes in the properties of the atmosphere  ... 
doi:10.1109/taes.2009.5259174 fatcat:cvxyf4qdevff3a6mukewsiw5ke

Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter [article]

Cong-Thanh Do, Tran Thien Dat Nguyen, Diluka Moratuwage, Changbeom Shim, Yon Dohn Chung
2022 arXiv   pre-print
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process.  ...  In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information.  ...  Non-linear dynamic model In this experiment, a multi-object tracking scenario involving 10 objects with each having a constant turn motion model is investigated.  ... 
arXiv:2008.00413v2 fatcat:lrv52qamqrb7bf7zht7s65cb7i

A Novel Stastical Particle Filtering Approach for Non-Linear and Non-Gaussian System Identification

Dhiraj K.Jha, Abhijit Verma, Avinash Kumar, Prabhat Panda
2012 International Journal of Computer Applications  
Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system.  ...  In this paper the particle filtering approach has been attempted for non-linear system identification.  ...  In this paper, the model is generated as a non-linear model and is tested using Kalman Filter approach. The particle Filter approach follows it for identification.  ... 
doi:10.5120/9700-4147 fatcat:p4gen3g4k5cjvgej3kkgq7u7iu

Multi-Bernoulli filtering with unknown clutter intensity and sensor field-of-view

Ba Tuong Vo, Ba Ngu Vo, Reza Hoseinnezhad, Ronald. P. S. Mahler
2011 2011 45th Annual Conference on Information Sciences and Systems  
In this paper we propose a multi-target filtering solution that can accommodate non-linear target model and unknown nonhomogeneous clutter intensity and sensor field-of-view.  ...  Significant mismatches in clutter and sensor field of view model parameters results in biased estimates.  ...  Demonstrations show that the proposed filter performs acceptably in a tracking scenario with non-linear target dynamics and measurements.  ... 
doi:10.1109/ciss.2011.5766180 dblp:conf/ciss/VoVHM11 fatcat:l2sv2ceebbfc5lpr4yotuug75a

Closed Form PHD Filtering for Linear Jump Markov Models

A. Pasha, B. Vo, H.d. Tuan, W.-k. Ma
2006 2006 9th International Conference on Information Fusion  
In particular, it has been discovered that the PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and birth.  ...  However, the previous work is not general enough to handle jump Markov systems (JMS), a popular approach to modeling maneuvering targets.  ...  In this approach the dynamics of a maneuvering target is modeled as a linear jump Markov system (LJMS), i.e. the target can switch between a set of linear models in a Markovian fashion.  ... 
doi:10.1109/icif.2006.301593 dblp:conf/fusion/PashaVTM06 fatcat:pw4ziefasrcd5kg4mekqbyel4u

A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models

Ba-Ngu Vo, Ahmed Pasha, Hoang Duong Tuan
2006 Proceedings of the 45th IEEE Conference on Decision and Control  
The PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and births.  ...  Our approach is based on a closed form solution to the PHD filter for linear Gaussian JMS multitarget model and the unscented transform.  ...  Linear Gaussian JMS multi-target model In the LGJMS multi-target model, each target follows a LGJMS model, i.e. the dynamics and measurement models for the kinematic state have the form: f k|k−1 (ξ|ξ ,  ... 
doi:10.1109/cdc.2006.377103 dblp:conf/cdc/VoPT06 fatcat:7uro7nipvncmdcbjxflnoqe2ha

A Particle Multi-Target Tracker for Superpositional Measurements using Labeled Random Finite Sets [article]

Francesco Papi, Du Yong Kim
2015 arXiv   pre-print
This modelling leads to a labeled version of Mahler's multi-target Bayes filter.  ...  We base our modelling on Labeled Random Finite Set (RFS) in order to jointly estimate the number of targets and their trajectories.  ...  A Nearly Constant Velocity (NCV) model is used to describe the target dynamics, while a zero-mean Gaussian random walk is used to model the fluctuations in time of the target complex amplitude, i.e., x  ... 
arXiv:1501.02248v2 fatcat:b77x4kgpsnfvzcyzaml7px4ehi

Bayesian multi-target tracking with superpositional measurements using labeled random finite sets

Francesco Papi, Du Yong Kim
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
In a superpositional sensor model, the measurement collected by the sensor at each time step is a superposition of measurements generated by each of the targets present in the surveillance area.  ...  We use the Bayes multi-target filter with Labeled Random Finite Set (RFS) in order to jointly estimate the number of targets and their trajectories.  ...  Additional details including the single-target dynamics and the TBD measurement model can be found in [20] .  ... 
doi:10.1109/eusipco.2015.7362777 dblp:conf/eusipco/PapiK15 fatcat:lpoq3gma2bcndgekygwngf2rwm

Automated Design of Robust Discriminant Analysis Classifier for Foot Pressure Lesions Using Kinematic Data

J.Y. Goulermas, A.H. Findlow, C.J. Nester, D. Howard, P. Bowker
2005 IEEE Transactions on Biomedical Engineering  
Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously.  ...  In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic  ...  A dynamic recurrent neural network was employed in [7] to predict kinematic variables from EMG data.  ... 
doi:10.1109/tbme.2005.851519 pmid:16189968 fatcat:wcy73xlk2fdunheqorewll6kby

A 6 DoF Navigation Algorithm for Autonomous Underwater Vehicles

Andrew K. Lammas, Karl Sammut, Fangpo He
2007 OCEANS 2007 - Europe  
In this paper, an autonomous underwater vehicle (AUV) is used as the dynamic system.  ...  Monte Carlo Localizer within the context of a navigation algorithm for a dynamic 6 DoF system.  ...  Future work will include analysis of the filters in this paper in a far more dynamic non-linear environment.  ... 
doi:10.1109/oceanse.2007.4302417 fatcat:wbvxbqej4rdmbmdgdgdvlsbvay
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