Approximate Bayesian computation with the Wasserstein distance

A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these d...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Jg. 81; H. 2; S. 235 - 269
Hauptverfasser:	Bernton, Espen, Jacob, Pierre E., Gerber, Mathieu, Robert, Christian P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Wiley 01.04.2019 Oxford University Press
Schlagworte:	Approximate Bayesian computation Bayesian analysis Bayesian theory Biology Computation Computer simulation Data data collection Datasets equations Generative models Hilbert curve Hilbert space Likelihood‐free inference Optimal transport Property Queueing Queues Regression analysis Statistical methods Statistical models Statistics Summaries Time series time series analysis Volatility Wasserstein distance
ISSN:	1369-7412, 1467-9868
Online-Zugang:	Volltext
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