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Approximate Bayes learning of stochastic differential equations
We introduce a nonparametric approach for estimating drift and diffusion functions in systems of stochastic differential equations from observations of the state vector.
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
We introduce a model where the rate of an inhomogeneous Poisson process is modified by a Chinese restaurant process.
Approximate Gaussian process inference for the drift function in stochastic differential equations
We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from incomplete observations of the state vector.
Inference in continuous-time change-point models
We consider the problem of Bayesian inference for continuous time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times.
Approximate inference in continuous time Gaussian-Jump processes
We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process.
