Probabilistic forecasting using Bayesian Model Averaging
Adrian E. Raftery, J. McLean Sloughter, Michael Polakowski, University of Washington
This package uses Bayesian Model Averaging to create probabilistic forecasts of ensembles using a mixture of normal distributions.

Chris Fraley, Adrian E. Raftery, J. McLean Sloughter,
Tilmann Gneiting, Michael Polakowski, University of Washington
Bayesian Model Averaging to create probabilistic
forecasts from ensemble forecasts and weather observations.

Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

Chris Fraley, Adrian E. Raftery, J. McLean Sloughter,
Tilmann Gneiting, University of Washington
With contributions from Bobby Yuen and Michael Polokowski

Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging
Chris Fraley, Adrian E. Raftery, J. McLean Sloughter, Tilmann
Gneiting, University of Washington With contributions from
Bobby Yuen and Michael Polokowski
Bayesian Model Averaging to create probabilistic forecasts
from ensemble forecasts and weather observations.

Chris Fraley, Adrian E. Raftery, J. McLean Sloughter, Tilmann
Gneiting, University of Washington.

Bayesian Model Averaging to create probabilistic forecasts
from ensemble forecasts and weather observations <https://stat.uw.edu/sites/default/files/files/reports/2007/tr516.pdf>.

Bayesian Model Averaging to create probabilistic forecasts
from ensemble forecasts and weather observations
<https://stat.uw.edu/sites/default/files/files/reports/2007/tr516.pdf>.

Functions for computing the ensembles of regularized linear
regression estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2017)
<arXiv:1712.03561>. The procedure works on top of a given penalized linear regression estimator,
the Elastic Net in this implementation, by fitting it to possibly overlapping subsets of features, while at the same
time encouraging diversity among the subsets, to reduce the correlations between the predictions
that result from each fitted model. The predictions from the models are then aggregated.