Derivative-Free Estimation of the Score Vector and Observed Information Matrix with Application to State-Space Models

Understand

Ionides, King et al.

• (see e.g.
• Inference for nonlinear dynamical systems, PNAS 103) have recently introduced an original approach to perform maximum likelihood parameter estimation in state-space models which only requires being able to simulate the latent Markov model according to its prior distribution.
• Their methodology relies on an approximation of the score vector for general statistical models based upon an artificial posterior distribution and bypasses the calculation of any derivative.