A Modularized Efficient Framework for Non-Markov Time Series Estimation

Understand

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.
• point process observation models used in neuroscience).