  We propose a technique for reformulation of state and parameter estimation
problems as that of matching explicitly computable definite integrals with
known kernels to data. The technique applies for a class of systems of
nonlinear ordinary differential equations and is aimed to exploit parallel
computational streams in order to increase speed of calculations. The idea is
based on the classical adaptive observers design. It has been shown that in
case the data is periodic it may be possible to reduce dimensionality of the
inference problem to that of the dimension of the vector of parameters entering
the right-hand side of the model nonlinearly. Performance and practical
implications of the method are illustrated on a benchmark model governing
dynamics of voltage in generated in barnacle giant muscle.
