Likelihood-Free Inference for Discrete Time Series Data Using Machine Learning
| dc.contributor.advisor | Black, Andrew | |
| dc.contributor.advisor | Maclean, John | |
| dc.contributor.author | O'Loughlin, Luke Phillip | |
| dc.contributor.school | School of Mathematical Sciences | en |
| dc.date.issued | 2023 | |
| dc.description.abstract | Mechanistic models are ubiquitous across many scientific domains, for example the susceptible-infectious-removed (SIR) model in epidemiology. Of great interest are the parameters of the mechanistic model, which often determine the specific behaviour that the system of interest displays. The parameters of mechanistic models are usually unobservable, hence they must be inferred from observed data. In particular, the Bayesian inference framework is popular when working with mechanistic models due to the conceptually simple way in which it allows one to marry prior information with data. Unfortunately, for many mechanistic models, performing Bayesian inference is computationally expensive due to unobserved latent variables in the model. In this thesis we develop a procedure to perform (approximate) Bayesian inference for models yielding a time series of discrete observations, using machine learning to address the scalability issues with traditional approaches such as particle marginal Metropolis Hastings (PMMH). We approach this problem by constructing a convolutional neural network based surrogate likelihood model which can evaluate the likelihood of the entire time series using a single neural network evaluation. This is in contrast to similar surrogate likelihood approaches to inference, which usually require one to additionally design/learn informative summary statistics from the time series data. We test our methods on a series of inference experiments using simulated data from epidemiological models, obtaining accurate results compared to the exact inference algorithm PMMH. Furthermore, we demonstrate that the runtime of our inference methods scale significantly better than PMMH with increasing dataset sizes, and with increasing model complexity. We also show how to extend our methods such that they work with multidimensional (discrete) time series data, and we demonstrate this by performing inference experiments on simulated data from a two-dimensional predator-prey model. Our methods obtain accurate results and significantly better runtimes than PMMH on the predator-prey experiments. | en |
| dc.description.dissertation | Thesis (M.Phil.) -- University of Adelaide, School of Mathematical Sciences, 2023 | en |
| dc.identifier.uri | https://hdl.handle.net/2440/139500 | |
| dc.language.iso | en | en |
| dc.provenance | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals | en |
| dc.subject | Bayesian inference | en |
| dc.subject | machine learning | en |
| dc.subject | epidemiology | en |
| dc.title | Likelihood-Free Inference for Discrete Time Series Data Using Machine Learning | en |
| dc.type | Thesis | en |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- O'Loughlin2023_MPhil.pdf
- Size:
- 4.43 MB
- Format:
- Adobe Portable Document Format
- Description: