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In Bayesian inference, predictive distributions are typically in the form of samples generated via Markov chain Monte Carlo (MCMC) or related algorithms.
Forecasts and verifications in Western Australia
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Verification of forecasts expressed in terms of probability
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Information Theory and Statistics
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Admissible probability measurement procedures
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Expected information as expected utility
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Bayesianly justifiable and relevant frequency calculations for the applied statistician
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Density Estimation for Statistics and Data Analysis
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On Kullback-Leibler loss and density estimation
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Nonparametric estimation in mixing sequences of random variables
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Nonparametric Curve Estimation from Time Series
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Predictive fit for natural exponential families
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Sampling-based approaches to calculating marginal densities
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On correcting for variance inflation in kernel density estimation
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A reliable data-based bandwidth selection method for kernel density estimation
Sheather, S. J. and Jones, M. C. (1991) · 1991
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Distribution estimation consistent in total variation and in two types of information divergence
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Practical Markov chain Monte Carlo
Geyer, C. J. (1992) · 1992
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Density estimation in the L ∞ L^{\infty} norm for dependent data with applications to the Gibbs sampler
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Subsampling the Gibbs sampler
MacEachern, S. N. and Berliner, L. M. (1994) · 1994
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Markov chains for exploring posterior distributions
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Comparing predictive accuracy
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A kernel estimator for discrete distributions
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Rosenthal, J. S. (1995) · 1995
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Posterior predictive assessment of model fitness via realized discrepancies
Gelman, A., Meng, X.-L. and Stern, H. (1996) · 1996
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Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds.) (1996) · 1996
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Strong convergence of sums of α \alpha -mixing random variables with applications to density estimation
Liebscher, E. (1996) · 1996
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Normal scale mixtures and dual probability densities
Gneiting, T. (1997) · 1997
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Evaluating density forecasts with applications to financial risk management
Diebold, F. X., Gunther, T. A. and Tay, A. S. (1998) · 1998
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Coherent dispersion criteria for optimal experimental design
Dawid, A. P. and Sebastiani, P. (1999) · 1999
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Density estimation under constraints
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Conditional forecasts in dynamic multivariate models
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Contemporary Bayesian Econometrics and Statistics
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Tests of conditional predictive ability
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The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification
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Calibrated Bayes: A Bayes/frequentist roadmap
Little, R. J. (2006) · 2006
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All of Nonparametric Statistics
Wasserman, L. (2006) · 2006
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Comparing density forecasts via weighted likelihood ratio tests
Amisano, G. and Giacomini, R. (2007) · 2007
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Strictly proper scoring rules, prediction, and estimation
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Uniform convergence rates for kernel estimation with dependent data
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A guide to Bayesian model selection for ecologists
Hooten, M. B. and Hobbs, N. T. (2015) · 2015
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Spatial dynamic factor analysis
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Learning, forecasting and structural breaks
Maheu, J. M. and Gordon, S. (2008) · 2008
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Bayesian density forecasting of intraday electricity prices using multivariate skew t t distributions
Panagiotelis, A. and Smith, M. (2008) · 2008
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Predictive model assessment for count data
Czado, C., Gneiting, T. and Held, L. (2009) · 2009
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Robust Statistics
Huber, P. J. and Ronchetti, E. M. (2009) · 2009
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Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities
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The contribution of structural break models to forecasting macroeconomic series
Bauwens, L., Koop, G., Korobilis, D. and Rombouts, J. V. K. (2015) · 2015
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Point and density forecasts for the Euro area using Bayesian VARs
Berg, T. O. and Henzel, S. R. (2015) · 2015
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Macroeconomic forecasting performance under alternative specifications of time-varying volatility
Clark, T. E. and Ravazzolo, F. (2015) · 2015
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Prior selection for vector autoregressions
Giannone, D., Lenza, M. and Primiceri, G. E. (2015) · 2015
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Regression-based covariance functions for nonstationary spatial modeling
Risser, M. D. and Calder, C. A. (2015) · 2015
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Bayesian forecasting using spatio-temporal models with applications to ozone levels in the eastern United States
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