text_combined: cedric archambeau nalvaoddappcsuclacuk apologies crossposting call abstracts nips 2006 workshop dynamical systems stochastic processes bayesian inference httpwwwcsuclacukstaffcarchambeaudsbhtm december 89 whistler bc canada abstract submission deadline november 1 2006 overview modelling continuoustime dynamical systems uncertain observations important task comes wide range applications ranging numerical weather prediction finance genetic networks motion capture video often may assume dynamical models formulated systems differential equations bayesian approach may incorporate priori knowledge dynamics providing probability distributions unknown functions correspond example driving forces appear coefficients parameters differential equations hence functions become stochastic processes probabilistic bayesian framework gaussian processes gps provide natural flexible framework circumstances use gps learning functions data wellestablished technique machine learning nevertheless application dynamical systems becomes highly nontrivial dynamics nonlinear gaussian parameter functions happens naturally nonlinear systems driven gaussian noise process nonlinearity needed provide necessary constraints eg positivity parameter functions case prior process systems dynamics nongaussian right start means closed form analytical posterior predictions even case gaussian observation noise longer possible moreover computation requires entire underlying gaussian latent process times discrete observation times hence inference dynamics would require nontrivial sampling methods approximation techniques raises following questions practical relevance nonlinear effects ie could ignore sample randomly posterior continuoustime processes deal large data sets andor high dimensional data functional laplace approximations suitable think variational approximations parameter hyperparameter estimation etc aim workshop provide forum discussing open problems related continuoustime stochastic dynamical systems links bayesian inference relevance machine learning workshop interest workers bayesian inference stochastic processes hope workshop provide new insights continoustime stochastic processes serve starting point new research perspectives future collaborations submissions welcome extended abstract submissions nips 2006 workshop dynamical systems stochastic processes bayesian inference following related areas restricted nonlinear dynamical systems bayesian inference stochastic processes gaussian nongaussian processes continuoustime markov chains continuoustime discretecontinuous state processes gaussian mixture gaussians nonparametric belief networks nonlinear filteringsmoothing suggested abstract length 4 pages maximum 8 pages formatted nips format abstracts made available web authors submit extended abstract kmxcsuclacuk pdf nov 1 2006 2359 utc email confirming reception submission sent organizers requests suggestions comments sent dsbcsuclacuk schedule oct 01 call extended abstracts nov 01 abstract submission deadline nov 17 notification acceptance nov 24 final extended abstracts due dec 8 9 workshop program order encourage active participation attendees morning afternoon session include invited talks short peerreviewed spotlights presentations extended poster sessions informal discussions workshop close wrapup speakers neil lawrence university sheffield manfred opper technical university berlin chris williams university edingburgh organizers cedric archambeau university college london manfred opper technical university berlin john shawetaylor university college london program committee cedric archambeau university college london dan cornford aston university manfred opper technical university berlin john shawetaylor university college london magnus rattray university manchester thu 07 aug 2008 112957 0100 uai call abstracts nips 06 worskshop dynamical systems stochastic processes bayesian inference