Estimating parameters in diffusion processes using an approximate maximum likelihood approach
We present an approximate Maximum Likelihood estimator for univariate Ito stochastic differential equations driven by Brownian motion, based on numerical calculation of the likelihood function. The transition probability density of a stochastic differential equation is given by the Kolmogorov forward equation, known as the Fokker-Planck equation. This partial differential equation can only be solv
