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Paul Birrell

2 accepted papers

2024

Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs

AISTATS 2024poster

Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable model, such as a Hidden Markov model (HMM), these methods are des…

2021

Variational inference for nonlinear ordinary differential equations

AISTATS 2021poster

We apply the reparameterisation trick to obtain a variational formulation of Bayesian inference in nonlinear ODE models. By invoking the linear noise approximation we also extend this variational formulation to a stochastic kinetic model. Our proposed inference method does not depend on any emulatio…