Computational Complexity and Parallelization in Bayesian Econometric Analysis
Department of Quantitative Economics, School of Business and Economics, Maastricht University, Maastricht, 6211LM, The Netherlands
Department of Economics, University Ca’ Foscari of Venice, Venice, 31022, Italy
Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, 39100, Italy
Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands
Tinbergen Institute, Amsterdam, 1082 MS, The Netherlands
Econometric Institute, Erasmus School of Economics, Erasmus University, Rotterdam, 3062 PA, The Netherlands
Author to whom correspondence should be addressed.
Academic Editor: Kerry Patterson
Received: 27 January 2016 / Revised: 28 January 2016 / Accepted: 3 February 2016 / Published: 22 February 2016
Note: In lieu of an abstract, this is an excerpt from the first page.
Challenging statements have appeared in recent years in the literature on advances in computational procedures.[...]
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Baştürk, N.; Casarin, R.; Ravazzolo, F.; van Dijk, H.K. Computational Complexity and Parallelization in Bayesian Econometric Analysis. Econometrics 2016, 4, 9.
Baştürk N, Casarin R, Ravazzolo F, van Dijk HK. Computational Complexity and Parallelization in Bayesian Econometric Analysis. Econometrics. 2016; 4(1):9.
Baştürk, Nalan; Casarin, Roberto; Ravazzolo, Francesco; van Dijk, Herman K. 2016. "Computational Complexity and Parallelization in Bayesian Econometric Analysis." Econometrics 4, no. 1: 9.
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