Learn More
Classical time-evolving spectral analysis techniques utilize a sliding window approach that fails to exploit overarching spectrotemporal structures that are known to occur in many real-world signals. In particular, many biological signals have the distinct quality of having few defining spectral characteristics. We propose an algorithm for efficiently(More)
We present a compartmentalized approach to finding the maximum a-posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g. group sparsity) and/or non-Gaussian measurement models (e.g.(More)
  • 1