MCMC inference of the shape and variability of time-response signals
Signals in response to time-localized events of a common phenomenon tend to exhibit a common shape, but with variable time scale, amplitude, and delay across trials in many domains. We develop a new formulation to learn the common shape and variables from noisy signal samples with a Bayesian signal...
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| creator | Katz-Rogozhnikov, Dmitriy A. Varshney, Kush R. Mojsilovic, Aleksandra Singh, Moninder |
| description | Signals in response to time-localized events of a common phenomenon tend to exhibit a common shape, but with variable time scale, amplitude, and delay across trials in many domains. We develop a new formulation to learn the common shape and variables from noisy signal samples with a Bayesian signal model and a Markov chain Monte Carlo inference scheme involving Gibbs sampling and independent Metropolis-Hastings. Our experiments with generated and real-world data show that the algorithm is robust to missing data, outperforms the existing approaches and produces easily interpretable outputs. |
| doi_str_mv | 10.1109/ICASSP.2011.5947218 |
| format | Conference Proceeding |
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| ispartof | 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, p.3956-3959 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_5947218 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Companies Delay Markov chain Monte Carlo Markov processes multiple alignment Outsourcing Shape Spline time-response signal |
| title | MCMC inference of the shape and variability of time-response signals |
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