On  inference for partially observed non-linear diffusion models 
                   using the Metropolis-Hastings algorithm

                          Osnat Stramer
         Department of Statistics and Actuarial Science
		   University of Iowa


 We develop  a new approach for the inference  of a broad
class of  non-linear continuous time models, when the data are observed
at discrete time points.  We      employ  a  Bayesian approach for
model estimation based on   MCMC methods. We use the
Hastings-Metropolis algorithm  with collection of Brownian bridges
and  linear diffusion bridges as   candidates  for the  
independent sampler to obtain
data    augmentation algorithms  for the missing paths between any two
observations.  We thus obtain  algorithms  which are independent of the
sample intervals. Our  approaches are illustrated with examples 
involving simulated data, and are applied to two  data sets:  the  
US interest rates and
the IBM closing stock prices respectively.