Journal article icon

Journal article

Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python

Abstract:

Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adap...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

Actions


Access Document


Files:
Publisher copy:
10.1016/j.epidem.2018.05.009

Authors


More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM; BDI-NDM
Role:
Author
ORCID:
0000-0001-5962-4238
Bill and Melinda Gates Foundation More from this funder
Publisher:
Elsevier Publisher's website
Journal:
Epidemics Journal website
Volume:
25
Pages:
80-88
Publication date:
2018-05-26
Acceptance date:
2018-05-24
DOI:
EISSN:
1878-0067
ISSN:
1755-4365
Pubs id:
pubs:854084
URN:
uri:2fa6a606-0694-4f3a-a368-12f82e06cff7
UUID:
uuid:2fa6a606-0694-4f3a-a368-12f82e06cff7
Local pid:
pubs:854084

Terms of use


Metrics


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP