Deep-learning-accelerated Bayesian inference for state-space models
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Hölén Hannouch, Elias
Holmstedt, Oskar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Bayesian inference is an important statistical tool for estimating uncertainties in
model parameters from data. One very important method is the Metropolis-Hastings
algorithm, which allows for parameter inference when analytical solutions are intractable.
The only requirement is that the likelihood function can be evaluated.
However, it is a computationally expensive algorithm, as it is usually run for several
thousand iterations. This is especially true for inference in state-space models, where
the likelihood is computed via Bayesian filtering, which is a costly operation in and
of itself. We propose a new method for doing Bayesian inference via Metropolis-
Hastings for state-space models by replacing the standard likelihood computation
with a neural network. The network is trained on data generated by a much shorter
earlier run of Metropolis-Hastings.
We show, both qualitatively and quantitatively, that our method produces comparable
results to the traditional method for several models. Moreover, our results
indicate that the performance of our method is consistent as the dimensionality of
the state-space model increases.
Finally, we show that our method is much more computationally efficient than
the traditional method for large runs. We investigate at what point our method
becomes the preferable alternative and find that the threshold occurs at quite small
runs, both in terms of computational time and desired output size.
Beskrivning
Ämne/nyckelord
Bayesian inference, MCMC, Metropolis-Hastings, state-space models, Bayesian filtering, surrogate likelihood, deep learning