E. Solak, D. J. Leith, R. Murray-smith, W. E. Leithead, C. E. Rasmussen
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
E. Solak, D. J. Leith, R. Murray-smith, W. E. Leithead, C. E. Rasmussen
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
E. Solak, W. E. Leithead, D. J. Leith
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
E. Solak, R. Murray-smith, W. E. Leithead, D. J. Leith, C. E. Rasmussen
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
Derivative observations in Gaussian Process models of dynamic systems (2003)
Solak, E, Murray-Smith, R., Leithead, W., Leith, D.
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
Derivative observations in Gaussian Process models of dynamic systems (2003)
Solak, E., Murray-Smith, R., Leithead, W.E., Leith, D.J., Rasmussen, C.E.
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
Divide and conquer identification using Gaussian process priors (2002)
Leith, D. J., Leithead, W. E., Solak, E., Murray-Smith, R.
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is well known that the equilibrium linearisations of a system do not uniquely specify the global...
Divide and conquer identification using Gaussian process priors
Leith, D. J., Leithead, W. E., Solak, E., Murray-Smith, R.
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is well known that the equilibrium linearisations of a system do not uniquely specify the global...
Derivative observations in Gaussian Process models of dynamic systems
Solak, E., Murray-Smith, R., Leithead, W.E., Leith, D.J., Rasmussen, C.E.
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...
Divide and conquer identification using Gaussian process priors
Leith, D. J., Leithead, W. E., Solak, E., Murray-Smith, R.
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is well known that the equilibrium linearisations of a system do not uniquely specify the global...
Derivative observations in Gaussian Process models of dynamic systems
Solak, E., Murray-Smith, R., Leithead, W.E., Leith, D.J., Rasmussen, C.E.
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular...