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We study the problem of estimating the parameters of a regression model from a set of observations, each consisting of a response and a predictor.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
W. R. Thompson · 1933
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A new approach to linear filtering and prediction problems
R. E. Kalman · 1960
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Generalized linear models
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C. N. Morris · 1982
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Natural exponential families with quadratic variance functions: statistical theory
C. N. Morris · 1983
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Dynamic generalized linear models and bayesian forecasting
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Posterior mode estimation by extended kalman filtering for multivariate dynamic generalized linear models
L. Fahrmeir · 1992
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Bayesian Forecasting & Dynamic Models
J. Harrison and M. West · 1999
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A contextual-bandit approach to personalized news article recommendation
L. Li, W. Chu, J. Langford, and R. E. Schapire · 2010
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An empirical evaluation of thompson sampling
O. Chapelle and L. Li · 2011
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Time series analysis by state space methods
J. Durbin and S. J. Koopman · 2012
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Further optimal regret bounds for thompson sampling
S. Agrawal and N. Goyal · 2013
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Thompson sampling for contextual bandits with linear payoffs
S. Agrawal and N. Goyal · 2013
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Graphical models, exponential families, and variational inference
M. J. Wainwright and M. I. Jordan · 2008
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Thompson sampling for 1-dimensional exponential family bandits
N. Korda, E. Kaufmann, and R. Munos · 2013
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