Jordan Franks

Newcastle University


Unbiased inference for discretely observed hidden Markov model diffusions


Statistics Seminar


31st January 2020, 3:00 pm – 3:45 pm
Fry Building, G.13


Diffusion processes observed with noise in the real world represent
challenging models for Bayesian parameter inference in the presence of
non-linearity, non-Gaussianity, and multi-dimensionality. We propose a
general inference approach for diffusion processes observed with noise,
thus opening up the possibility of inference for such challenging
models. The algorithm is based on a clever combination of popular
algorithms, such as pseudo-marginal and adaptive Markov chain Monte
Carlo, Euler-Maruyama discretisations, particle filters and coupling,
and multi-level Monte Carlo. The algorithm is therefore generally
programmable, and we give a small set of conditions under which the
approach is guaranteed to deliver unbiased model inference. We also
consider the efficiency of the approach within the multi-level
framework. We then apply our algorithm on some example models. This
talk is based on joint work with Ajay Jasra, Kody J.H. Law and Matti Vihola.

Authors:
Jordan Franks, Ajay Jasra, Kody J.H. Law and Matti Vihola

Presenter:
Jordan Franks

Affiliation:
Newcastle University

Email:
jordan.franks@newcastle.ac.uk





Organiser: Song Liu

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