A bayesian forecasting model for sequential bidding

1991 ◽  
Vol 10 (6) ◽  
pp. 565-577 ◽  
Author(s):  
D. N. Attwell ◽  
J. Q. Smith
Keyword(s):  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Sequential Bidding

Bayesian Forcasting Model – 2020 Presidential Elections in the United States of America

2021 ◽  
Vol 704 (1) ◽  
pp. 91-104
Author(s):  
Maria Raczyńska
Keyword(s):  
United States ◽  
United States Of America ◽  
Presidential Elections ◽  
Bayesian Models ◽  
The United States ◽  
Point Of View ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Technical Point ◽  
Made In

The article describes and explains a prior centric Bayesian forecasting model for the 2020 US elections.The model is based on the The Economist forecasting project, but strongly differs from it. From the technical point of view, it uses R and Stan programming and Stan software. The article’s focus is on theoretical decisions made in the process of constructing the model and outcomes. It describes why Bayesian models are used and how they are used to predict US presidential elections.

scholarly journals A Bayesian forecasting model of international conflict

2019 ◽  
Vol 17 (3) ◽  
pp. 235-242
Author(s):  
George W Williford ◽  
Douglas B Atkinson
Keyword(s):  
International Relations ◽  
Bayesian Methods ◽  
International Conflict ◽  
Rare Events ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Strong Interest ◽  
Out Of Sample ◽  
Cross National

Scholars and practitioners in international relations have a strong interest in forecasting international conflict. However, due to the complexity of forecasting rare events, existing attempts to predict the onset of international conflict in a cross-national setting have generally had low rates of success. In this paper, we apply Bayesian methods to develop a forecasting model designed to predict the onset of international conflict at the yearly level. We find that this model performs substantially better at producing accurate predictions both in and out of sample.

A bayesian forecasting model for house account sales

Omega ◽  
1978 ◽  
Vol 6 (5) ◽  
pp. 455-457
Author(s):  
Derek W Bunn
Keyword(s):  
Forecasting Model ◽  
Bayesian Forecasting

The use of a bayesian forecasting model in the management of warfarin therapy after total hip arthroplasty

1999 ◽  
Vol 14 (8) ◽  
pp. 988-993 ◽  
Author(s):  
G.D. Motykie ◽  
D. Mokhtee ◽  
L.P. Zebala ◽  
J.A. Caprini ◽  
J.C. Kudrna ◽  
...  
Keyword(s):  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Warfarin Therapy ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Total Hip

scholarly journals An Updated Dynamic Bayesian Forecasting Model for the US Presidential Election

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Merlin Heidemanns ◽  
Andrew Gelman ◽  
G. Elliott Morris
Keyword(s):  
Presidential Election ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
The Us ◽  
Us Presidential Election

scholarly journals Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals

2018 ◽  
Vol 27 (2) ◽  
pp. 255-262 ◽  
Author(s):  
Lukas F. Stoetzer ◽  
Marcel Neunhoeffer ◽  
Thomas Gschwend ◽  
Simon Munzert ◽  
Sebastian Sternberg
Keyword(s):  
Public Opinion ◽  
Bayesian Approach ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Forecasting Models ◽  
Ex Ante ◽  
Multiparty Systems ◽  
Public Opinion Polls ◽  
Opinion Polls ◽  
Quantities Of Interest

We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.

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