Zobrazeno 1 - 10
of 212
pro vyhledávání: '"Martin, Gael M."'
Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic forecasts i
Externí odkaz:
http://arxiv.org/abs/2311.01021
We demonstrate that the forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to the empirical finding that predictions formed by combining multiple fore
Externí odkaz:
http://arxiv.org/abs/2308.05263
This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the predictive performance of optimal forecasts from potentially misspeci
Externí odkaz:
http://arxiv.org/abs/2303.01651
Autor:
Martin, Gael M., Frazier, David T., Maneesoonthorn, Worapree, Loaiza-Maya, Ruben, Huber, Florian, Koop, Gary, Maheu, John, Nibbering, Didier, Panagiotelis, Anastasios
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified expli
Externí odkaz:
http://arxiv.org/abs/2212.03471
Publikováno v:
Statistical Science, 2023
This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first confronted by Bayes in 1763, we highlight the key contributions of: Lapl
Externí odkaz:
http://arxiv.org/abs/2208.00646
We investigate the performance and sampling variability of estimated forecast combinations, with particular attention given to the combination of forecast distributions. Unknown parameters in the forecast combination are optimized according to criter
Externí odkaz:
http://arxiv.org/abs/2206.02376
Autor:
Pesonen, Henri, Simola, Umberto, Köhn-Luque, Alvaro, Vuollekoski, Henri, Lai, Xiaoran, Frigessi, Arnoldo, Kaski, Samuel, Frazier, David T., Maneesoonthorn, Worapree, Martin, Gael M., Corander, Jukka
Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computatio
Externí odkaz:
http://arxiv.org/abs/2112.12841
Publikováno v:
Statistical Science, 2023
The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statistical problems that challenge exact methods like Markov chain Monte Car
Externí odkaz:
http://arxiv.org/abs/2112.10342
Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy i
Externí odkaz:
http://arxiv.org/abs/2106.12262
Autor:
Martin, Gael M., Frazier, David T., Maneesoonthorn, Worapree, Loaiza-Maya, Rubén, Huber, Florian, Koop, Gary, Maheu, John, Nibbering, Didier, Panagiotelis, Anastasios
Publikováno v:
In International Journal of Forecasting April-June 2024 40(2):811-839
