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Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated...
Persistent link: https://www.econbiz.de/10010706614
Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated...
Persistent link: https://www.econbiz.de/10009002734
En estimation bayésienne, lorsque le calcul explicite de la loi a posteriori du vecteur des paramètres à estimer est impossible, les méthodes de Monte-Carlo par chaînes de Markov (MCMC) [Robert and Casella, 1999] permettent théoriquement de fournir un échantillon approximativement...
Persistent link: https://www.econbiz.de/10009002735
For numerous models, it is impossible to conduct an exact Bayesian inference. There are many cases where the derivation of the posterior distribution leads to intractable calculations (due to the fact that this generally involves intractable integrations). The Bayesian computational literature...
Persistent link: https://www.econbiz.de/10011073850