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 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.