Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes

Understand

We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter.

• The estimation is to be done online, i.e.
• the parameter estimate should be updated recursively based on the observation filtration.
• We provide a theoretical analysis of the stochastic gradient ascent algorithm on the incomplete-data log-likelihood.