Baio, G;
Cerina, R;
(2015)
A predictable outcome.
Significance
, 12
(2)
pp. 11-13.
10.1111/j.1740-9713.2015.00810.x.
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Abstract
Gianluca Baio and Roberto Cerina used a modified version of a dynamic Bayesian forecasting model to "predict" the 2014 US Senate elections. The results bode well for the 2016 vote.
| Type: | Article |
|---|---|
| Title: | A predictable outcome |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/j.1740-9713.2015.00810.x |
| Publisher version: | http://dx.doi.org/10.1111/j.1740-9713.2015.00810.x |
| Language: | English |
| Additional information: | This is the peer reviewed version of the following article: Baio, G. and Cerina, R. (2015), A predictable outcome. Significance, 12: 11–13., which has been published in final form at doi: 10.1111/j.1740-9713.2015.00810.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1470383 |
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