Probabilistic Forecasting and Comparative Model Assessment Based on Markov Chain Monte Carlo Output

24 Aug 2016 Fabian Krüger Sebastian Lerch Thordis L. Thorarinsdottir Tilmann Gneiting

In Bayesian inference, predictive distributions are typically available only through a sample generated via Markov chain Monte Carlo (MCMC) or related algorithms. In this paper, we conduct a systematic analysis of how to make and evaluate probabilistic forecasts from such simulation output... (read more)

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  • METHODOLOGY