@InProceedings{ip-Urteaga2016,
  author    = {I{\~n}igo Urteaga and M\'{o}nica F. Bugallo and Petar M Djuri\'{c}},
  title     = {{Sequential Monte Carlo methods under model uncertainty}},
  booktitle = {2016 IEEE Statistical Signal Processing Workshop (SSP)},
  year      = {2016},
  pages     = {1-5},
  month     = {June},
  abstract  = {We propose a Sequential Monte Carlo (SMC) method for filtering and prediction of time-varying signals under model uncertainty. Instead of resorting to model selection, we fuse the information from the considered models within the proposed SMC method. We achieve our goal by dynamically adjusting the resampling step according to the posterior predictive power of each model, which is updated sequentially as we observe more data. The method allows the models with better predictive powers to explore the state space with more resources than models lacking predictive power. This is done autonomously and dynamically within the SMC method. We show the validity of the presented method by evaluating it on an illustrative application.},
  doi       = {10.1109/SSP.2016.7551747},
  keywords  = {Monte Carlo methods;filtering theory;prediction theory;signal sampling;state-space methods;SMC method;information fusion;model uncertainty;resampling step;sequential Monte Carlo method;state-space method;time-varying signal filtering;time-varying signal prediction;Atmospheric measurements;Computational modeling;Mathematical model;Monte Carlo methods;Particle measurements;Predictive models;Uncertainty;Sequential Monte Carlo;dynamic model averaging;information fusion;particle filtering;resampling},
  owner     = {iurteaga},
  timestamp = {2016-04-29},
}

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M"],"year":2016,"bibtype":"inproceedings","biburl":"https://iurteaga.github.io/myConferences.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Iñigo"],"propositions":[],"lastnames":["Urteaga"],"suffixes":[]},{"firstnames":["Mónica","F."],"propositions":[],"lastnames":["Bugallo"],"suffixes":[]},{"firstnames":["Petar","M"],"propositions":[],"lastnames":["Djurić"],"suffixes":[]}],"title":"Sequential Monte Carlo methods under model uncertainty","booktitle":"2016 IEEE Statistical Signal Processing Workshop (SSP)","year":"2016","pages":"1-5","month":"June","abstract":"We propose a Sequential Monte Carlo (SMC) method for filtering and prediction of time-varying signals under model uncertainty. Instead of resorting to model selection, we fuse the information from the considered models within the proposed SMC method. We achieve our goal by dynamically adjusting the resampling step according to the posterior predictive power of each model, which is updated sequentially as we observe more data. The method allows the models with better predictive powers to explore the state space with more resources than models lacking predictive power. This is done autonomously and dynamically within the SMC method. We show the validity of the presented method by evaluating it on an illustrative application.","doi":"10.1109/SSP.2016.7551747","keywords":"Monte Carlo methods;filtering theory;prediction theory;signal sampling;state-space methods;SMC method;information fusion;model uncertainty;resampling step;sequential Monte Carlo method;state-space method;time-varying signal filtering;time-varying signal prediction;Atmospheric measurements;Computational modeling;Mathematical model;Monte Carlo methods;Particle measurements;Predictive models;Uncertainty;Sequential Monte Carlo;dynamic model averaging;information fusion;particle filtering;resampling","owner":"iurteaga","timestamp":"2016-04-29","bibtex":"@InProceedings{ip-Urteaga2016,\n author = {I{\\~n}igo Urteaga and M\\'{o}nica F. Bugallo and Petar M Djuri\\'{c}},\n title = {{Sequential Monte Carlo methods under model uncertainty}},\n booktitle = {2016 IEEE Statistical Signal Processing Workshop (SSP)},\n year = {2016},\n pages = {1-5},\n month = {June},\n abstract = {We propose a Sequential Monte Carlo (SMC) method for filtering and prediction of time-varying signals under model uncertainty. Instead of resorting to model selection, we fuse the information from the considered models within the proposed SMC method. We achieve our goal by dynamically adjusting the resampling step according to the posterior predictive power of each model, which is updated sequentially as we observe more data. The method allows the models with better predictive powers to explore the state space with more resources than models lacking predictive power. This is done autonomously and dynamically within the SMC method. We show the validity of the presented method by evaluating it on an illustrative application.},\n doi = {10.1109/SSP.2016.7551747},\n keywords = {Monte Carlo methods;filtering theory;prediction theory;signal sampling;state-space methods;SMC method;information fusion;model uncertainty;resampling step;sequential Monte Carlo method;state-space method;time-varying signal filtering;time-varying signal prediction;Atmospheric measurements;Computational modeling;Mathematical model;Monte Carlo methods;Particle measurements;Predictive models;Uncertainty;Sequential Monte Carlo;dynamic model averaging;information fusion;particle filtering;resampling},\n owner = {iurteaga},\n timestamp = {2016-04-29},\n}\n\n","author_short":["Urteaga, I.","Bugallo, M. F.","Djurić, P. 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