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Erscheinungsjahr: 
2007
Schriftenreihe/Nr.: 
cemmap working paper No. CWP12/07
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
In this paper we examine the implications of the statistical large sample theory for the computational complexity of Bayesian and quasi-Bayesian estimation carried out using Metropolis random walks. Our analysis is motivated by the Laplace-Bernstein-Von Mises central limit theorem, which states that in large samples the posterior or quasi-posterior approaches a normal density. Using this observation, we establish polynomial bounds on the computational complexity of general Metropolis random walks methods in large samples. Our analysis covers cases, where the underlying log-likelihood or extremum criterion function is possibly nonconcave, discontinuous, and of increasing dimension. However, the central limit theorem restricts the deviations from continuity and log-concavity of the log-likelihood or extremum criterion function in a very specific manner.
Schlagwörter: 
Monte Carlo , Computational Complexity , Curved Exponential
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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