Simon J. Godsill mainly investigates Algorithm, Markov chain Monte Carlo, Particle filter, Speech recognition and Artificial intelligence. His Algorithm research is multidisciplinary, incorporating elements of Bayes estimator, Filter, Image fusion, Mathematical optimization and Posterior probability. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Auxiliary particle filter and Maximum likelihood sequence estimation.
The various areas that Simon J. Godsill examines in his Markov chain Monte Carlo study include Prior probability, Markov chain, Autoregressive model and Gibbs sampling. His biological study spans a wide range of topics, including Smoothing, Point and Monte Carlo method, Importance sampling. Simon J. Godsill interconnects Machine learning, Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
Simon J. Godsill mainly focuses on Algorithm, Artificial intelligence, Particle filter, Markov chain Monte Carlo and Monte Carlo method. His Algorithm research includes elements of Smoothing, Filter, Speech recognition, Mathematical optimization and Kalman filter. Audio signal is closely connected to Pattern recognition in his research, which is encompassed under the umbrella topic of Artificial intelligence.
Simon J. Godsill has included themes like State-space representation, Control theory, Posterior probability and Importance sampling in his Particle filter study. His Markov chain Monte Carlo research focuses on Markov process and how it connects with Markov chain. His Monte Carlo method study incorporates themes from Sampling, Estimation theory and Object detection.
His primary scientific interests are in Algorithm, Particle filter, Inference, Artificial intelligence and Applied mathematics. The Algorithm study combines topics in areas such as Prior probability, Markov chain Monte Carlo, Image fusion, Bayesian inference and Nonlinear system. His work carried out in the field of Markov chain Monte Carlo brings together such families of science as Estimation theory and Gibbs sampling.
His research in Particle filter intersects with topics in Stochastic differential equation, Linear system, Smoothing and Monte Carlo method. The study incorporates disciplines such as Machine learning, Audio signal, Computer vision and Pattern recognition in addition to Artificial intelligence. The various areas that he examines in his Applied mathematics study include Time series, Stochastic process, Mathematical optimization, Posterior probability and Series.
Simon J. Godsill spends much of his time researching Artificial intelligence, Inference, Algorithm, Particle filter and Image fusion. His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition. His studies deal with areas such as Video tracking, Machine learning, Bayesian probability and Gesture as well as Inference.
Simon J. Godsill combines subjects such as Image resolution, Hyperspectral imaging and Mixture model with his study of Algorithm. He has researched Particle filter in several fields, including Filtering problem, Monte Carlo method, Importance sampling and Mathematical optimization. His Monte Carlo method study integrates concerns from other disciplines, such as Smoothing and State space.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
On sequential Monte Carlo sampling methods for Bayesian filtering
Arnaud Doucet;Simon Godsill;Christophe Andrieu.
Statistics and Computing (2000)
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
O. Cappe;S.J. Godsill;E. Moulines.
Proceedings of the IEEE (2007)
On sequential simulation-based methods for Bayesian filtering
Arnaud Doucet;Simon J. Godsill;Christophe Andrieu.
Statistics and Computing (1998)
Monte Carlo Smoothing for Nonlinear Time Series
Simon J Godsill;Arnaud Doucet;Mike West.
Journal of the American Statistical Association (2004)
Digital Audio Restoration
Simon Godsill;Peter Rayner;Olivier Cappé.
Monte Carlo filtering for multi target tracking and data association
J. Vermaak;S.J. Godsill;P. Perez.
IEEE Transactions on Aerospace and Electronic Systems (2005)
Trans-dimensional Markov chain Monte Carlo
Peter J. Green;Simon Godsill;Juha Heikkinen.
On the Relationship Between Markov chain Monte Carlo Methods for Model Uncertainty
Simon J Godsill.
Journal of Computational and Graphical Statistics (2001)
Poisson models for extended target and group tracking
Kevin Gilholm;Simon Godsill;Simon Maskell;David Salmond.
Proceedings of SPIE (2005)
Digital Audio Restoration: A Statistical Model Based Approach
Simon H. Godsill;P. J. Rayner.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: