%0 Journal Article %1 Rosa_2004_1173 %A Rosales, Rafael A %D 2004 %J Bull. Math. Biol. %K 15294422 Bayes Biological, Carlo Chains, Channels, Comparative Ion Markov Method, Models, Monte Statistical, Study, Theorem, %N 5 %P 1173--1199 %R 10.1016/j.bulm.2003.12.001 %T MCMC for hidden Markov models incorporating aggregation of states and filtering. %U http://dx.doi.org/10.1016/j.bulm.2003.12.001 %V 66 %X This paper is concerned with the statistical analysis of single ion channel records. Single channels are modelled by using hidden Markov models and a combination of Bayesian statistics and Markov chain Monte Carlo methods. The techniques presented here provide a straightforward generalization to those in Rosales et al. (2001, Biophys. J., 80, 1088-1103), allowing to consider constraints imposed by a gating mechanism such as the aggregation of states into classes. This paper also presents an extension that allows to consider correlated background noise and filtered data, extending the scope of the analysis toward real experimental conditions. The methods described here are based on a solid probabilistic basis and are less computationally intensive than alternative Bayesian treatments or frequentist approaches that consider correlated data.

@article{Rosa_2004_1173, abstract = {This paper is concerned with the statistical analysis of single ion channel records. Single channels are modelled by using hidden Markov models and a combination of Bayesian statistics and Markov chain Monte Carlo methods. The techniques presented here provide a straightforward generalization to those in Rosales et al. (2001, Biophys. J., 80, 1088-1103), allowing to consider constraints imposed by a gating mechanism such as the aggregation of states into classes. This paper also presents an extension that allows to consider correlated background noise and filtered data, extending the scope of the analysis toward real experimental conditions. The methods described here are based on a solid probabilistic basis and are less computationally intensive than alternative Bayesian treatments or frequentist approaches that consider correlated data.}, added-at = {2009-06-03T11:20:58.000+0200}, author = {Rosales, Rafael A}, biburl = {https://www.bibsonomy.org/bibtex/28e6728b12222bcf836b4af1e14a37f7f/hake}, description = {The whole bibliography file I use.}, doi = {10.1016/j.bulm.2003.12.001}, interhash = {5acb4e1cf0ce150f8266e08fb6b4c386}, intrahash = {8e6728b12222bcf836b4af1e14a37f7f}, journal = {Bull. Math. Biol.}, keywords = {15294422 Bayes Biological, Carlo Chains, Channels, Comparative Ion Markov Method, Models, Monte Statistical, Study, Theorem,}, month = Sep, number = 5, pages = {1173--1199}, pii = {S009282400300137X}, pmid = {15294422}, timestamp = {2009-06-03T11:21:27.000+0200}, title = {MCMC for hidden Markov models incorporating aggregation of states and filtering.}, url = {http://dx.doi.org/10.1016/j.bulm.2003.12.001}, volume = 66, year = 2004 }