Bayesian inference for nonlinear multivariate diffusion models observed with error
Computational Statistics & Data Analysis
Computational methods for complex stochastic systems: a review of some alternatives to MCMC
Statistics and Computing
Bayesian calibration for Monte Carlo localization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Particle filtering for dynamic agent modelling in simplified poker
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A Bayesian approach to joint tracking and identification of geometric shapes in video sequences
Image and Vision Computing
Node localization during power adjustment in wireless sensor networks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A monte carlo algorithm for state and parameter estimation of extended targets
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Support vector machine adaptive control of nonlinear systems
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Direct fitting of dynamic models using integrated nested Laplace approximations - INLA
Computational Statistics & Data Analysis
Sequential support vector machine control of nonlinear systems by state feedback
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Sequential parameter learning and filtering in structured autoregressive state-space models
Statistics and Computing
Modeling and prediction of nonlinear environmental system using Bayesian methods
Computers and Electronics in Agriculture
Automatica (Journal of IFAC)
Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks
Journal of Signal Processing Systems
| Hi-index | 35.69 |
Particle filters for dynamic state-space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, real-time applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some low-dimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state-space models. Marginalizing the static parameters avoids the problem of impoverishment, which typically occurs when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results