Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data

Predicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be observed in batches, each of which is based on a large number...

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Veröffentlicht in:	Journal of forecasting 2024-09, Vol.43 (6), p.1814-1834
Hauptverfasser:	Deb, Soudeep, Roy, Rishideep, Das, Shubhabrata
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis Convergence election data Election forecasting Elections forecasting from partial information Gibbs sampling hierarchical priors Normality Partial information posterior convergence Probability Simulation
Online-Zugang:	Volltext
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